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Stirling RE, Nurse ES, Payne D, Naim-Feil J, Coleman H, Freestone DR, Richarson MP, Brinkmann BH, D'Souza WJ, Grayden DB, Cook MJ, Karoly PJ. User experience of a seizure risk forecasting app: A mixed methods investigation. Epilepsy Behav 2024; 157:109876. [PMID: 38851123 DOI: 10.1016/j.yebeh.2024.109876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 05/30/2024] [Accepted: 06/02/2024] [Indexed: 06/10/2024]
Abstract
OBJECTIVE Over recent years, there has been a growing interest in exploring the utility of seizure risk forecasting, particularly how it could improve quality of life for people living with epilepsy. This study reports on user experiences and perspectives of a seizure risk forecaster app, as well as the potential impact on mood and adjustment to epilepsy. METHODS Active app users were asked to complete a survey (baseline and 3-month follow-up) to assess perspectives on the forecast feature as well as mood and adjustment. Post-hoc, nine neutral forecast users (neither agreed nor disagreed it was useful) completed semi-structured interviews, to gain further insight into their perspectives of epilepsy management and seizure forecasting. Non-parametric statistical tests and inductive thematic analyses were used to analyse the quantitative and qualitative data, respectively. RESULTS Surveys were completed by 111 users. Responders consisted of "app users" (n = 58), and "app and forecast users" (n = 53). Of the "app and forecast users", 40 % believed the forecast was accurate enough to be useful in monitoring for seizure risk, and 60 % adopted it for purposes like scheduling activities and helping mental state. Feeling more in control was the most common response to both high and low risk forecasted states. In-depth interviews revealed five broad themes, of which 'frustrations with lack of direction' (regarding their current epilepsy management approach), 'benefits of increased self-knowledge' and 'current and anticipated usefulness of forecasting' were the most common. SIGNIFICANCE Preliminary results suggest that seizure risk forecasting can be a useful tool for people with epilepsy to make lifestyle changes, such as scheduling daily events, and experience greater feelings of control. These improvements may be attributed, at least partly, to the improvements in self-knowledge experienced through forecast use.
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Affiliation(s)
- Rachel E Stirling
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia; Graeme Clark Institute of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia.
| | - Ewan S Nurse
- Graeme Clark Institute of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia; Seer Medical, Melbourne, Victoria, Australia; Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Australia.
| | | | - Jodie Naim-Feil
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia; Graeme Clark Institute of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia.
| | - Honor Coleman
- Melbourne School of Psychological Sciences, The University of Melbourne, Melbourne, Victoria, Australia; Epilepsy Research Centre, Department of Medicine (Austin Health), University of Melbourne, Victoria, Melbourne, Australia; Department of Neuroscience, Faculty of Medicine, Nursing & Health Science, Monash University, Melbourne, Australia.
| | | | | | | | - Wendyl J D'Souza
- Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Australia.
| | - David B Grayden
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia; Graeme Clark Institute of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia; Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Australia.
| | - Mark J Cook
- Graeme Clark Institute of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia; Seer Medical, Melbourne, Victoria, Australia; Department of Medicine, St Vincent's Hospital Melbourne, The University of Melbourne, Australia.
| | - Philippa J Karoly
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia; Graeme Clark Institute of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia.
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Fu A, Lado FA. Seizure Detection, Prediction, and Forecasting. J Clin Neurophysiol 2024; 41:207-213. [PMID: 38436388 DOI: 10.1097/wnp.0000000000001045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2024] Open
Abstract
SUMMARY Among the many fears associated with seizures, patients with epilepsy are greatly frustrated and distressed over seizure's apparent unpredictable occurrence. However, increasing evidence have emerged over the years to support that seizure occurrence is not a random phenomenon as previously presumed; it has a cyclic rhythm that oscillates over multiple timescales. The pattern in rises and falls of seizure rate that varies over 24 hours, weeks, months, and years has become a target for the development of innovative devices that intend to detect, predict, and forecast seizures. This article will review the different tools and devices available or that have been previously studied for seizure detection, prediction, and forecasting, as well as the associated challenges and limitations with the utilization of these devices. Although there is strong evidence for rhythmicity in seizure occurrence, very little is known about the mechanism behind this oscillation. This article concludes with early insights into the regulations that may potentially drive this cyclical variability and future directions.
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Affiliation(s)
- Aradia Fu
- Department of Neurology, Zucker School of Medicine at Hofstra-Northwell, Great Neck, New York, U.S.A
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Seth EA, Watterson J, Xie J, Arulsamy A, Md Yusof HH, Ngadimon IW, Khoo CS, Kadirvelu A, Shaikh MF. Feasibility of cardiac-based seizure detection and prediction: A systematic review of non-invasive wearable sensor-based studies. Epilepsia Open 2024; 9:41-59. [PMID: 37881157 PMCID: PMC10839362 DOI: 10.1002/epi4.12854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 10/21/2023] [Indexed: 10/27/2023] Open
Abstract
A reliable seizure detection or prediction device can potentially reduce the morbidity and mortality associated with epileptic seizures. Previous findings indicating alterations in cardiac activity during seizures suggest the usefulness of cardiac parameters for seizure detection or prediction. This study aims to examine available studies on seizure detection and prediction based on cardiac parameters using non-invasive wearable devices. The Embase, PubMed, and Scopus databases were used to systematically search according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Human studies that evaluated seizure detection or prediction based on cardiac parameters collected using wearable devices were included. The QUADAS-2 tool and proposed standards for validation for seizure detection devices were used for quality assessment. Twenty-four articles were identified and included in the analysis. Twenty studies evaluated seizure detection algorithms, and four studies focused on seizure prediction. Most studies used either a wrist-worn or chest-worn device for data acquisition. Among the seizure detection studies, cardiac parameters utilized for the algorithms mainly included heart rate (HR) (n = 11) or a combination of HR and heart rate variability (HRV) (n = 6). HR-based seizure detection studies collectively reported a sensitivity range of 56%-100% and a false alarm rate (FAR) of 0.02-8/h, with most studies performing retrospective validation of the algorithms. Three of the seizure prediction studies retrospectively validated multimodal algorithms, combining cardiac features with other physiological signals. Only one study prospectively validated their seizure prediction algorithm using HRV extracted from ECG data collected from a custom wearable device. These studies have demonstrated the feasibility of using cardiac parameters for seizure detection and prediction with wearable devices, with varying algorithmic performance. Many studies are in the proof-of-principle stage, and evidence for real-time detection or prediction is currently limited. Future studies should prioritize further refinement of the algorithm performance with prospective validation using large-scale longitudinal data. PLAIN LANGUAGE SUMMARY: This systematic review highlights the potential use of wearable devices, like wristbands, for detecting and predicting seizures via the measurement of heart activity. By reviewing 24 articles, it was found that most studies focused on using heart rate and changes in heart rate for seizure detection. There was a lack of studies looking at seizure prediction. The results were promising but most studies were not conducted in real-time. Therefore, more real-time studies are needed to verify the usage of heart activity-related wearable devices to detect seizures and even predict them, which will be beneficial to people with epilepsy.
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Affiliation(s)
- Eryse Amira Seth
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Jessica Watterson
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Department of Human‐Centred ComputingMonash UniversityMelbourneVictoriaAustralia
| | - Jue Xie
- Department of Human‐Centred ComputingMonash UniversityMelbourneVictoriaAustralia
| | - Alina Arulsamy
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Hadri Hadi Md Yusof
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Irma Wati Ngadimon
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Ching Soong Khoo
- Neurology Unit, Department of MedicineUniversiti Kebangsaan Malaysia Medical CentreKuala LumpurMalaysia
| | - Amudha Kadirvelu
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
| | - Mohd Farooq Shaikh
- Neuropharmacology Research Laboratory, Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- Jeffrey Cheah School of Medicine and Health SciencesMonash University MalaysiaBandar SunwayMalaysia
- School of Dentistry and Medical SciencesCharles Sturt UniversityOrangeNew South WalesAustralia
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4
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Batista J, Pinto MF, Tavares M, Lopes F, Oliveira A, Teixeira C. EEG epilepsy seizure prediction: the post-processing stage as a chronology. Sci Rep 2024; 14:407. [PMID: 38172583 PMCID: PMC10764904 DOI: 10.1038/s41598-023-50609-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 12/21/2023] [Indexed: 01/05/2024] Open
Abstract
Almost one-third of epileptic patients fail to achieve seizure control through anti-epileptic drug administration. In the scarcity of completely controlling a patient's epilepsy, seizure prediction plays a significant role in clinical management and providing new therapeutic options such as warning or intervention devices. Seizure prediction algorithms aim to identify the preictal period that Electroencephalogram (EEG) signals can capture. However, this period is associated with substantial heterogeneity, varying among patients or even between seizures from the same patient. The present work proposes a patient-specific seizure prediction algorithm using post-processing techniques to explore the existence of a set of chronological events of brain activity that precedes epileptic seizures. The study was conducted with 37 patients with Temporal Lobe Epilepsy (TLE) from the EPILEPSIAE database. The designed methodology combines univariate linear features with a classifier based on Support Vector Machines (SVM) and two post-processing techniques to handle pre-seizure temporality in an easily explainable way, employing knowledge from network theory. In the Chronological Firing Power approach, we considered the preictal as a sequence of three brain activity events separated in time. In the Cumulative Firing Power approach, we assumed the preictal period as a sequence of three overlapping events. These methodologies were compared with a control approach based on the typical machine learning pipeline. We considered a Seizure Prediction horizon (SPH) of 5 mins and analyzed several values for the Seizure Occurrence Period (SOP) duration, between 10 and 55 mins. Our results showed that the Cumulative Firing Power approach may improve the seizure prediction performance. This new strategy performed above chance for 62% of patients, whereas the control approach only validated 49% of its models.
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Affiliation(s)
- Joana Batista
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.
| | - Mauro F Pinto
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Mariana Tavares
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - Fábio Lopes
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
- Epilepsy Center, Department Neurosurgery, Medical Center-University of Freiburg , Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ana Oliveira
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
| | - César Teixeira
- Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
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5
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Baud MO, Proix T, Gregg NM, Brinkmann BH, Nurse ES, Cook MJ, Karoly PJ. Seizure forecasting: Bifurcations in the long and winding road. Epilepsia 2023; 64 Suppl 4:S78-S98. [PMID: 35604546 PMCID: PMC9681938 DOI: 10.1111/epi.17311] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/20/2022] [Accepted: 05/20/2022] [Indexed: 11/28/2022]
Abstract
To date, the unpredictability of seizures remains a source of suffering for people with epilepsy, motivating decades of research into methods to forecast seizures. Originally, only few scientists and neurologists ventured into this niche endeavor, which, given the difficulty of the task, soon turned into a long and winding road. Over the past decade, however, our narrow field has seen a major acceleration, with trials of chronic electroencephalographic devices and the subsequent discovery of cyclical patterns in the occurrence of seizures. Now, a burgeoning science of seizure timing is emerging, which in turn informs best forecasting strategies for upcoming clinical trials. Although the finish line might be in view, many challenges remain to make seizure forecasting a reality. This review covers the most recent scientific, technical, and medical developments, discusses methodology in detail, and sets a number of goals for future studies.
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Affiliation(s)
- Maxime O Baud
- Sleep-Wake-Epilepsy Center, Center for Experimental Neurology, NeuroTec, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
- Wyss Center for Bio- and Neuro-Engineering, Geneva, Switzerland
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan S Nurse
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Mark J Cook
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
| | - Philippa J Karoly
- Graeme Clark Institute, University of Melbourne, Melbourne, Victoria, Australia
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6
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Viana PF, Attia TP, Nasseri M, Duun-Henriksen J, Biondi A, Winston JS, Martins IP, Nurse ES, Dümpelmann M, Schulze-Bonhage A, Freestone DR, Kjaer TW, Richardson MP, Brinkmann BH. Seizure forecasting using minimally invasive, ultra-long-term subcutaneous electroencephalography: Individualized intrapatient models. Epilepsia 2023; 64 Suppl 4:S124-S133. [PMID: 35395101 PMCID: PMC9547037 DOI: 10.1111/epi.17252] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 04/05/2022] [Accepted: 04/05/2022] [Indexed: 11/30/2022]
Abstract
OBJECTIVE One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. METHODS We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. RESULTS Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. SIGNIFICANCE This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.
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Affiliation(s)
- Pedro F. Viana
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Tal Pal Attia
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Mona Nasseri
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- School of Engineering, University of North Florida, Jacksonville, Florida, USA
| | | | - Andrea Biondi
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
| | - Joel S. Winston
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
| | | | - Ewan S. Nurse
- Seer Medical, Melbourne, Victoria, Australia
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Matthias Dümpelmann
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Dean R. Freestone
- Seer Medical, Melbourne, Victoria, Australia
- Department of Medicine, University of Melbourne, Melbourne, Victoria, Australia
| | - Troels W. Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Mark P. Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital National Health Service Foundation Trust, London, UK
- National Institute for Health Research Biomedical Research Centre at South London and Maudsley National Health Service Foundation Trust, London, UK
| | - Benjamin H. Brinkmann
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
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Andrzejak RG, Zaveri HP, Schulze‐Bonhage A, Leguia MG, Stacey WC, Richardson MP, Kuhlmann L, Lehnertz K. Seizure forecasting: Where do we stand? Epilepsia 2023; 64 Suppl 3:S62-S71. [PMID: 36780237 PMCID: PMC10423299 DOI: 10.1111/epi.17546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 02/06/2023] [Indexed: 02/14/2023]
Abstract
A lot of mileage has been made recently on the long and winding road toward seizure forecasting. Here we briefly review some selected milestones passed along the way, which were discussed at the International Conference for Technology and Analysis of Seizures-ICTALS 2022-convened at the University of Bern, Switzerland. Major impetus was gained recently from wearable and implantable devices that record not only electroencephalography, but also data on motor behavior, acoustic signals, and various signals of the autonomic nervous system. This multimodal monitoring can be performed for ultralong timescales covering months or years. Accordingly, features and metrics extracted from these data now assess seizure dynamics with a greater degree of completeness. Most prominently, this has allowed the confirmation of the long-suspected cyclical nature of interictal epileptiform activity, seizure risk, and seizures. The timescales cover daily, multi-day, and yearly cycles. Progress has also been fueled by approaches originating from the interdisciplinary field of network science. Considering epilepsy as a large-scale network disorder yielded novel perspectives on the pre-ictal dynamics of the evolving epileptic brain. In addition to discrete predictions that a seizure will take place in a specified prediction horizon, the community broadened the scope to probabilistic forecasts of a seizure risk evolving continuously in time. This shift of gears triggered the incorporation of additional metrics to quantify the performance of forecasting algorithms, which should be compared to the chance performance of constrained stochastic null models. An imminent task of utmost importance is to find optimal ways to communicate the output of seizure-forecasting algorithms to patients, caretakers, and clinicians, so that they can have socioeconomic impact and improve patients' well-being.
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Grants
- NIH NS109062 NIH HHS
- MR/N026063/1 Medical Research Council
- R01 NS109062 NINDS NIH HHS
- R01 NS094399 NINDS NIH HHS
- NIH NS094399 NIH HHS
- Medical Research Council Centre for Neurodevelopmental Disorders
- National Health and Medical Research Council
- National Institutes of Health
- University of Bern, the Inselspital, University Hospital Bern, the Alliance for Epilepsy Research, the Swiss National Science Foundation, UCB, FHC, the Wyss Center for bio‐ and neuro‐engineering, the American Epilepsy Society (AES), the CURE epilepsy Foundation, Ripple neuro, Sintetica, DIXI medical, UNEEG medical and NeuroPace.
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Affiliation(s)
- Ralph G. Andrzejak
- Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
| | | | - Andreas Schulze‐Bonhage
- Epilepsy Center, NeurocenterUniversity Medical Center, University of FreiburgFreiburgGermany
| | - Marc G. Leguia
- Department of Information and Communication TechnologiesUniversitat Pompeu FabraBarcelonaSpain
| | - William C. Stacey
- Department of Neurology, Department of Biomedical EngineeringBioInterfaces Institute, University of MichiganAnn ArborMichiganUSA
- Division of NeurologyVA Ann Arbor Medical CenterAnn ArborMichiganUSA
| | - Mark P. Richardson
- School of NeuroscienceInstitute of Psychiatry Psychology and Neuroscience, King's College LondonLondonUK
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information TechnologyMonash UniversityClaytonVictoriaAustralia
| | - Klaus Lehnertz
- Department of EpileptologyUniversity of Bonn Medical CentreBonnGermany
- Helmholtz Institute for Radiation and Nuclear PhysicsUniversity of BonnBonnGermany
- Interdisciplinary Center for Complex SystemsUniversity of BonnBonnGermany
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8
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Leguia MG, Rao VR, Tcheng TK, Duun-Henriksen J, Kjaer TW, Proix T, Baud MO. Learning to generalize seizure forecasts. Epilepsia 2023; 64 Suppl 4:S99-S113. [PMID: 36073237 DOI: 10.1111/epi.17406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Revised: 09/06/2022] [Accepted: 09/06/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVE Epilepsy is characterized by spontaneous seizures that recur at unexpected times. Nonetheless, using years-long electroencephalographic (EEG) recordings, we previously found that patient-reported seizures consistently occur when interictal epileptiform activity (IEA) cyclically builds up over days. This multidien (multiday) interictal-ictal relationship, which is shared across patients, may bear phasic information for forecasting seizures, even if individual patterns of seizure timing are unknown. To test this rigorously in a large retrospective dataset, we pretrained algorithms on data recorded from a group of patients, and forecasted seizures in other, previously unseen patients. METHODS We used retrospective long-term data from participants (N = 159) in the RNS System clinical trials, including intracranial EEG recordings (icEEG), and from two participants in the UNEEG Medical clinical trial of a subscalp EEG system (sqEEG). Based on IEA detections, we extracted instantaneous multidien phases and trained generalized linear models (GLMs) and recurrent neural networks (RNNs) to forecast the probability of seizure occurrence at a 24-h horizon. RESULTS With GLMs and RNNs, seizures could be forecasted above chance in 79% and 81% of previously unseen subjects with a median discrimination of area under the curve (AUC) = .70 and .69 and median Brier skill score (BSS) = .07 and .08. In direct comparison, individualized models had similar median performance (AUC = .67, BSS = .08), but for fewer subjects (60%). Moreover, calibration of pretrained models could be maintained to accommodate different seizure rates across subjects. SIGNIFICANCE Our findings suggest that seizure forecasting based on multidien cycles of IEA can generalize across patients, and may drastically reduce the amount of data needed to issue forecasts for individuals who recently started collecting chronic EEG data. In addition, we show that this generalization is independent of the method used to record seizures (patient-reported vs. electrographic) or IEA (icEEG vs. sqEEG).
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Affiliation(s)
- Marc G Leguia
- Wyss Center Fellow, Sleep-Wake-Epilepsy Center, Center for Experimental Neurology, NeuroTec, Department of Neurology, Inselspital Bern University Hospital, University of Bern, Bern, Switzerland
| | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, University of California, San Francisco, California, USA
| | | | | | - Troels W Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center and Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
- Wyss Center for Bio and Neuroengineering, Geneva, Switzerland
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9
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Attia TP, Viana PF, Nasseri M, Duun-Henriksen J, Biondi A, Winston JS, Martins IP, Nurse ES, Dümpelmann M, Worrell GA, Schulze-Bonhage A, Freestone DR, Kjaer TW, Brinkmann BH, Richardson MP. Seizure forecasting using minimally invasive, ultra-long-term subcutaneous EEG: Generalizable cross-patient models. Epilepsia 2023; 64 Suppl 4:S114-S123. [PMID: 35441703 PMCID: PMC9582039 DOI: 10.1111/epi.17265] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 04/17/2022] [Accepted: 04/18/2022] [Indexed: 11/29/2022]
Abstract
This study describes a generalized cross-patient seizure-forecasting approach using recurrent neural networks with ultra-long-term subcutaneous EEG (sqEEG) recordings. Data from six patients diagnosed with refractory epilepsy and monitored with an sqEEG device were used to develop a generalized algorithm for seizure forecasting using long short-term memory (LSTM) deep-learning classifiers. Electrographic seizures were identified by a board-certified epileptologist. One-minute data segments were labeled as preictal or interictal based on their relationship to confirmed seizures. Data were separated into training and testing data sets, and to compensate for the unbalanced data ratio in training, noise-added copies of preictal data segments were generated to expand the training data set. The mean and standard deviation (SD) of the training data were used to normalize all data, preserving the pseudo-prospective nature of the analysis. Different architecture classifiers were trained and tested using a leave-one-patient-out cross-validation method, and the area under the receiver-operating characteristic (ROC) curve (AUC) was used to evaluate the performance classifiers. The importance of each input signal was evaluated using a leave-one-signal-out method with repeated training and testing for each classifier. Cross-patient classifiers achieved performance significantly better than chance in four of the six patients and an overall mean AUC of 0.602 ± 0.126 (mean ± SD). A time in warning of 37.386% ± 5.006% (mean ± std) and sensitivity of 0.691 ± 0.068 (mean ± std) were observed for patients with better than chance results. Analysis of input channels showed a significant contribution (p < .05) by the Fourier transform of signals channels to overall classifier performance. The relative contribution of input signals varied among patients and architectures, suggesting that the inclusion of all signals contributes to robustness in a cross-patient classifier. These early results show that it is possible to forecast seizures training with data from different patients using two-channel ultra-long-term sqEEG.
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Affiliation(s)
- Tal Pal Attia
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA
| | - Pedro F. Viana
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital NHS Foundation Trust, London, UK
- Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Mona Nasseri
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA
- School of Engineering, University of North Florida, Jacksonville, Florida, USA
| | | | - Andrea Biondi
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Joel S. Winston
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital NHS Foundation Trust, London, UK
| | - Isabel P. Martins
- Centro de Estudos Egas Moniz, Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Ewan S. Nurse
- Seer Medical Pty Ltd., Melbourne, Victoria, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center, Faculty of Medicine, University Medical Center, University of Freiburg, Freiburg, Germany
| | - Gregory A. Worrell
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center, Faculty of Medicine, University Medical Center, University of Freiburg, Freiburg, Germany
| | - Dean R. Freestone
- Seer Medical Pty Ltd., Melbourne, Victoria, Australia
- Department of Medicine, The University of Melbourne, Melbourne, Victoria, Australia
| | - Troels W. Kjaer
- Department of Neurology, Zealand University Hospital, Roskilde, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Lab, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark P. Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
- Centre for Epilepsy, King’s College Hospital NHS Foundation Trust, London, UK
- NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust, London, UK
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10
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Li Y, Zhao X. Patient-specific warning of epileptic seizure upon shapelets features. Heliyon 2023; 9:e22431. [PMID: 38034613 PMCID: PMC10687046 DOI: 10.1016/j.heliyon.2023.e22431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 12/02/2023] Open
Abstract
Epilepsy is an intractable chronic neurological disease attached to extensive attention. Due to the fact that unpredictable seizure attacks result in serious physical injuries, early warning before seizure occurrence can help patients to get timely treatment and intervention. This paper presents a novel patient-specific method to predict epileptic seizures by learning shapelets of scalp electroencephalogram (EEG) signals recorded from different channels. In the proposed method, EEG signals are preprocessed to raise the Signal to Noise Rate (SNR). Multichannel shapelets space is constructed by the learning-near-to-optimal shapelets method. EEG signals are converted to distance matrices by projecting them on the shapelets' space. Bi-LSTM, SVM, CNN, and an ensemble of them are used to classify the feature set. Based on the prediction results then raise alarms. The proposed methodology is applied to the CHB-MIT scalp EEG dataset of 10 cases. The proposed method achieves a sensitivity of 91.33% and a false prediction rate of 0.16 h-1.
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Affiliation(s)
- Yingxiang Li
- School of Mathematics and Statistics, Lanzhou University, Lanzhou, PR China
| | - Xuejing Zhao
- School of Mathematics and Statistics, Lanzhou University, Lanzhou, PR China
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11
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Al-hajjar ALN, Al-Qurabat AKM. An overview of machine learning methods in enabling IoMT-based epileptic seizure detection. THE JOURNAL OF SUPERCOMPUTING 2023; 79:1-48. [PMID: 37359338 PMCID: PMC10123593 DOI: 10.1007/s11227-023-05299-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 06/28/2023]
Abstract
The healthcare industry is rapidly automating, in large part because of the Internet of Things (IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of Medical Things (IoMT). Data collecting and processing are the fundamental components of all IoMT applications. Machine learning (ML) algorithms must be included into IoMT immediately due to the vast quantity of data involved in healthcare and the value that precise forecasts have. In today's world, together, IoMT, cloud services, and ML techniques have become effective tools for solving many problems in the healthcare sector, such as epileptic seizure monitoring and detection. One of the biggest hazards to people's lives is epilepsy, a lethal neurological condition that has become a global issue. To prevent the deaths of thousands of epileptic patients each year, there is a critical necessity for an effective method for detecting epileptic seizures at their earliest stage. Numerous medical procedures, including epileptic monitoring, diagnosis, and other procedures, may be carried out remotely with the use of IoMT, which will reduce healthcare expenses and improve services. This article seeks to act as both a collection and a review of the different cutting-edge ML applications for epilepsy detection that are presently being combined with IoMT.
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Affiliation(s)
| | - Ali Kadhum M. Al-Qurabat
- Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq
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12
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Gleichgerrcht E, Dumitru M, Hartmann DA, Munsell BC, Kuzniecky R, Bonilha L, Sameni R. Seizure forecasting using machine learning models trained by seizure diaries. Physiol Meas 2022; 43:10.1088/1361-6579/aca6ca. [PMID: 36541513 PMCID: PMC9940727 DOI: 10.1088/1361-6579/aca6ca] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 11/28/2022] [Indexed: 11/30/2022]
Abstract
Objectives.People with refractory epilepsy are overwhelmed by the uncertainty of their next seizures. Accurate prediction of future seizures could greatly improve the quality of life for these patients. New evidence suggests that seizure occurrences can have cyclical patterns for some patients. Even though these cyclicalities are not intuitive, they can be identified by machine learning (ML), to identify patients with predictable vs unpredictable seizure patterns.Approach.Self-reported seizure logs of 153 patients from the Human Epilepsy Project with more than three reported seizures (totaling 8337 seizures) were used to obtain inter-seizure interval time-series for training and evaluation of the forecasting models. Two classes of prediction methods were studied: (1) statistical approaches using Bayesian fusion of population-wise and individual-wise seizure patterns; and (2) ML-based algorithms including least squares, least absolute shrinkage and selection operator, support vector machine (SVM) regression, and long short-term memory regression. Leave-one-person-out cross-validation was used for training and evaluation, by training on seizure diaries of all except one subject and testing on the left-out subject.Main results.The leading forecasting models were the SVM regression and a statistical model that combined the median of population-wise seizure time-intervals with a test subject's prior seizure intervals. SVM was able to forecast 50%, 70%, 81%, 84%, and 87% of seizures of unseen subjects within 0, 1, 2, 3 to 4 d of mean absolute forecasting error, respectively. The subject-wise performances show that patients with more frequent seizures were generally better predicted.Significance.ML models can leverage non-random patterns within self-reported seizure diaries to forecast future seizures. While diary-based seizure forecasting alone is only one of many aspects of clinical care of patients with epilepsy, studying the level of predictability across seizures and patients paves the path towards a better understanding of predictable vs unpredictable seizures on individualized and population-wise bases.
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Affiliation(s)
| | - Mircea Dumitru
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA
| | - David A. Hartmann
- Department of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA
| | - Brent C. Munsell
- Department of Computer Science, University of North Carolina, Chapel Hill, NC
| | | | - Leonardo Bonilha
- Department of Neurology, School of Medicine, Emory University, Atlanta, GA
| | - Reza Sameni
- Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA
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13
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Wang L, Zhu W, Wang R, Li W, Liang G, Ji Z, Dong X, Shi X. Suppressing interferences of EIT on synchronous recording EEG based on comb filter for seizure detection. Front Neurol 2022; 13:1070124. [DOI: 10.3389/fneur.2022.1070124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
Background and objectiveThe purpose of this study was to eliminate the interferences of electrical impedance tomography (EIT) on synchronous recording electroencephalography (EEG) for seizure detection.MethodsThe simulated EIT signal generated by COMSOL Multiphysics was superimposed on the clinical EEG signal obtained from the CHB-MIT Scalp EEG Database, and then the spectrum features of superimposed mixed signals were analyzed. According to the spectrum analysis, in addition to high-frequency interference at 51.2 kHz related to the drive current, there was also low-frequency interference caused by switching of electrode pairs, which were used to inject drive current. A low pass filter and a comb filter were used to suppress the high-frequency interference and low-frequency interference, respectively. Simulation results suggested the low-pass filter and comb filter working together effectively filtered out the interference of EIT on EEG in the process of synchronous monitoring.ResultsAs a result, the normal EEG and epileptic EEG could be recognized effectively. Pearson correlation analysis further confirmed the interference of EIT on EEG was effectively suppressed.ConclusionsThis study provides a simple and effective interference suppression method for the synchronous monitoring of EIT and EEG, which could be served as a reference for the synchronous monitoring of EEG and other medical electromagnetic devices.
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14
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Laiou P, Biondi A, Bruno E, Viana PF, Winston JS, Rashid Z, Ranjan Y, Conde P, Stewart C, Sun S, Zhang Y, Folarin A, Dobson RJB, Schulze-Bonhage A, Dümpelmann M, Richardson MP. Temporal Evolution of Multiday, Epileptic Functional Networks Prior to Seizure Occurrence. Biomedicines 2022; 10:biomedicines10102662. [PMID: 36289925 PMCID: PMC9599905 DOI: 10.3390/biomedicines10102662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 10/10/2022] [Accepted: 10/11/2022] [Indexed: 12/02/2022] Open
Abstract
Epilepsy is one of the most common neurological disorders, characterized by the occurrence of repeated seizures. Given that epilepsy is considered a network disorder, tools derived from network neuroscience may confer the valuable ability to quantify the properties of epileptic brain networks. In this study, we use well-established brain network metrics (i.e., mean strength, variance of strength, eigenvector centrality, betweenness centrality) to characterize the temporal evolution of epileptic functional networks over several days prior to seizure occurrence. We infer the networks using long-term electroencephalographic recordings from 12 people with epilepsy. We found that brain network metrics are variable across days and show a circadian periodicity. In addition, we found that in 9 out of 12 patients the distribution of the variance of strength in the day (or even two last days) prior to seizure occurrence is significantly different compared to the corresponding distributions on all previous days. Our results suggest that brain network metrics computed fromelectroencephalographic recordings could potentially be used to characterize brain network changes that occur prior to seizures, and ultimately contribute to seizure warning systems.
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Affiliation(s)
- Petroula Laiou
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Correspondence: (P.L.); (A.B.)
| | - Andrea Biondi
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Correspondence: (P.L.); (A.B.)
| | - Elisa Bruno
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Pedro F. Viana
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Faculty of Medicine, University of Lisbon, 1649-028 Lisbon, Portugal
| | - Joel S. Winston
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Zulqarnain Rashid
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Yatharth Ranjan
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Pauline Conde
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Callum Stewart
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Shaoxiong Sun
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Yuezhou Zhang
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
| | - Amos Folarin
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Institute of Health Informatics, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London SE5 8AF, UK
- Health Data Research UK London, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London W1T 7DN, UK
| | - Richard J. B. Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- Institute of Health Informatics, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London SE5 8AF, UK
- Health Data Research UK London, University College London, London WC1E 6BT, UK
- NIHR Biomedical Research Centre, University College London Hospitals NHS Foundation Trust, London W1T 7DN, UK
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Medical Center-University of Freiburg, Faculty of Medicine, University of Freiburg, 79106 Freiburg, Germany
- Department of Microsystems Engineering (IMTEK), University of Freiburg, 79110 Freiburg, Germany
| | - Mark P. Richardson
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King’s College, London SE5 8AF, UK
- NIHR Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, King’s College London, London SE5 8AF, UK
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15
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Li X, Tao S, Lhatoo SD, Cui L, Huang Y, Hampson JP, Zhang GQ. A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy. Front Big Data 2022; 5:965715. [PMID: 36059922 PMCID: PMC9428292 DOI: 10.3389/fdata.2022.965715] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 07/11/2022] [Indexed: 02/03/2023] Open
Abstract
Epilepsy affects ~2-3 million individuals in the United States, a third of whom have uncontrolled seizures. Sudden unexpected death in epilepsy (SUDEP) is a catastrophic and fatal complication of poorly controlled epilepsy and is the primary cause of mortality in such patients. Despite its huge public health impact, with a ~1/1,000 incidence rate in persons with epilepsy, it is an uncommon enough phenomenon to require multi-center efforts for well-powered studies. We developed the Multimodal SUDEP Data Resource (MSDR), a comprehensive system for sharing multimodal epilepsy data in the NIH funded Center for SUDEP Research. The MSDR aims at accelerating research to address critical questions about personalized risk assessment of SUDEP. We used a metadata-guided approach, with a set of common epilepsy-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) multi-site annotated datasets; (2) user interfaces for capturing, managing, and accessing data; and (3) computational approaches for the analysis of multimodal clinical data. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the MSDR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. MSDR prospectively integrated and curated epilepsy patient data from seven institutions, and it currently contains data on 2,739 subjects and 10,685 multimodal clinical data files with different data formats. In total, 55 users registered in the current MSDR data repository, and 6 projects have been funded to apply MSDR in epilepsy research, including three R01 projects and three R21 projects.
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Affiliation(s)
- Xiaojin Li
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Shiqiang Tao
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Samden D. Lhatoo
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Licong Cui
- Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States,School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Yan Huang
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Johnson P. Hampson
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Guo-Qiang Zhang
- Department of Neurology, The University of Texas Health Science Center at Houston, Houston, TX, United States,Texas Institute for Restorative Neurotechnologies, The University of Texas Health Science Center at Houston, Houston, TX, United States,School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States,*Correspondence: Guo-Qiang Zhang
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16
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Haneef Z, Yang K, Sheth SA, Aloor FZ, Aazhang B, Krishnan V, Karakas C. Sub-scalp electroencephalography: A next-generation technique to study human neurophysiology. Clin Neurophysiol 2022; 141:77-87. [PMID: 35907381 DOI: 10.1016/j.clinph.2022.07.003] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 06/20/2022] [Accepted: 07/03/2022] [Indexed: 11/29/2022]
Abstract
Sub-scalp electroencephalography (ssEEG) is emerging as a promising technology in ultra-long-term electroencephalography (EEG) recordings. Given the diversity of devices available in this nascent field, uncertainty persists about its utility in epilepsy evaluation. This review critically dissects the many proposed utilities of ssEEG devices including (1) seizure quantification, (2) seizure characterization, (3) seizure lateralization, (4) seizure localization, (5) seizure alarms, (6) seizure forecasting, (7) biomarker discovery, (8) sleep medicine, and (9) responsive stimulation. The different ssEEG devices in development have individual design philosophies with unique strengths and limitations. There are devices offering primarily unilateral recordings (24/7 EEGTM SubQ, NeuroviewTM, Soenia® UltimateEEG™), bilateral recordings (Minder™, Epios™), and even those with responsive stimulation capability (EASEE®). We synthesize the current knowledge of these ssEEG systems. We review the (1) ssEEG devices, (2) use case scenarios, (3) challenges and (4) suggest a roadmap for ideal ssEEG designs.
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Affiliation(s)
- Zulfi Haneef
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Kaiyuan Yang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA.
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX 77030, USA
| | - Fuad Z Aloor
- Department of Medicine, Baylor College of Medicine, Houston, TX 77030, USA
| | - Behnaam Aazhang
- Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Vaishnav Krishnan
- Department of Neurology, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - Cemal Karakas
- Division of Pediatric Neurology, Department of Neurology, University of Louisville, Louisville, KY 40202, USA; Norton Children's Neuroscience Institute, Louisville, KY 40241, USA
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17
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Abstract
PURPOSE OF REVIEW To review the mutual interactions between sleep and epilepsy, including mechanisms of epileptogenesis, the relationship between sleep apnea and epilepsy, and potential strategies to treat seizures. RECENT FINDINGS Recent studies have highlighted the role of functional network systems underlying epileptiform activation in sleep in several epilepsy syndromes, including absence epilepsy, benign focal childhood epilepsy, and epileptic encephalopathy with spike-wave activation in sleep. Sleep disorders are common in epilepsy, and early recognition and treatment can improve seizure frequency and potentially reduce SUDEP risk. Additionally, epilepsy is associated with cyclical patterns, which has led to new treatment approaches including chronotherapy, seizure monitoring devices, and seizure forecasting. Adenosine kinase and orexin receptor antagonists are also promising new potential drug targets that could be used to treat seizures. Sleep and epilepsy have a bidirectional relationship that intersects with many aspects of clinical management. In this article, we identify new areas of research involving future therapeutic opportunities in the field of epilepsy.
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18
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Pang TD, Nearing BD, Verrier RL, Schachter SC. T-wave heterogeneity crescendo in the surface EKG is superior to heart rate acceleration for seizure prediction. Epilepsy Behav 2022; 130:108670. [PMID: 35367725 DOI: 10.1016/j.yebeh.2022.108670] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 03/12/2022] [Accepted: 03/14/2022] [Indexed: 11/19/2022]
Abstract
We examined whether T-wave heterogeneity (TWH) on the surface electrocardiographic (EKG) could predict epileptic seizure onset. Patients with electroencephalography-confirmed generalized tonic-clonic seizures (GTCS) (n = 6) exhibited abnormal elevations in TWH (>80 µV) at baseline (105 ± 20.4 µV), which increased from 30 min prior to seizure without heart rate increases > 2 beats/min until 10 min pre-seizure. Specifically, TWH on 3-lead surface EKG patch recordings increased from 1-hour baseline to 30 min (<0.05), 20 min (p < 0.002), 10 min (p = 0.01), and 1 min (p = 0.01) before seizure onset. At 10 min following GTCS, TWH returned to 110 ± 20.3 µV, similar to baseline (p = 0.54). This pre-ictal TWH warning pattern was not present in patients with psychogenic nonepileptic seizures (PNES) (n = 3), as TWH did not increase until PNES and returned to baseline within 10 min after PNES. Acute elevations in TWH may predict impending GTCS and may discriminate patients with GTCS from those with behaviorally similar PNES.
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Affiliation(s)
- Trudy D Pang
- Department of Neurology, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA, United States; Harvard Medical School, 99 Brookline Avenue, RN-301, Boston, MA 02215, United States.
| | - Bruce D Nearing
- Harvard Medical School, 99 Brookline Avenue, RN-301, Boston, MA 02215, United States; Department of Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA, United States.
| | - Richard L Verrier
- Harvard Medical School, 99 Brookline Avenue, RN-301, Boston, MA 02215, United States; Department of Medicine, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA, United States.
| | - Steven C Schachter
- Department of Neurology, Beth Israel Deaconess Medical Center (BIDMC), Boston, MA, United States; Harvard Medical School, 99 Brookline Avenue, RN-301, Boston, MA 02215, United States; Massachusetts General Hospital, Boston, MA, United States; Consortia for Improving Medicine with Innovation & Technology (CIMIT), 125 Nashua St., Suite 324, Boston, MA 02114, United States.
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19
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Mao X, Wang X, Jin M, Li Q, Jia J, Li M, Zhou H, Liu Z, Jin W, Zhao Y, Luo Z. Critical involvement of lysyl oxidase in seizure-induced neuronal damage through ERK-Alox5-dependent ferroptosis and its therapeutic implications. Acta Pharm Sin B 2022; 12:3513-3528. [PMID: 36176900 PMCID: PMC9513491 DOI: 10.1016/j.apsb.2022.04.017] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Revised: 04/18/2022] [Accepted: 04/24/2022] [Indexed: 02/08/2023] Open
Abstract
Recent insights collectively suggest the important roles of lysyl oxidase (LysOX) in the pathological processes of several acute and chronic neurological diseases, but the molecular regulatory mechanisms remain elusive. Herein, we explore the regulatory role of LysOX in the seizure-induced ferroptotic cell death of neurons. Mechanistically, LysOX promotes ferroptosis-associated lipid peroxidation in neurons via activating extracellular regulated protein kinase (ERK)-dependent 5-lipoxygenase (Alox5) signaling. In addition, overexpression of LysOX via adeno-associated viral vector (AAV)-based gene transfer enhances ferroptosis sensitivity and aggravates seizure-induced hippocampal damage. Our studies show that pharmacological inhibition of LysOX with β-aminopropionitrile (BAPN) significantly blocks seizure-induced ferroptosis and thereby alleviates neuronal damage, while the BAPN-associated cardiotoxicity and neurotoxicity could further be reduced through encapsulation with bioresponsive amorphous calcium carbonate-based nanocarriers. These findings unveil a previously unrecognized LysOX-ERK-Alox5 pathway for ferroptosis regulation during seizure-induced neuronal damage. Suppressing this pathway may yield therapeutic implications for restoring seizure-induced neuronal injury.
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20
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Pinto MF, Leal A, Lopes F, Pais J, Dourado A, Sales F, Martins P, Teixeira CA. On the clinical acceptance of black-box systems for EEG seizure prediction. Epilepsia Open 2022; 7:247-259. [PMID: 35377561 PMCID: PMC9159247 DOI: 10.1002/epi4.12597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/07/2022] [Accepted: 03/31/2022] [Indexed: 11/06/2022] Open
Abstract
Seizure prediction may be the solution for epileptic patients whose drugs and surgery do not control seizures. Despite 46 years of research, few devices/systems underwent clinical trials and/or are commercialized, where the most recent state-of-the-art approaches, as neural networks models, are not used to their full potential. The latter demonstrates the existence of social barriers to new methodologies due to data bias, patient safety, and legislation compliance. In the form of literature review, we performed a qualitative study to analyze the seizure prediction ecosystem to find these social barriers. With the Grounded Theory, we draw hypotheses from data, while with the Actor-Network Theory we considered that technology shapes social configurations and interests, being fundamental in healthcare. We obtained a social network that describes the ecosystem and propose research guidelines aiming at clinical acceptance. Our most relevant conclusion is the need for model explainability, but not necessarily intrinsically interpretable models, for the case of seizure prediction. Accordingly, we argue that it is possible to develop robust prediction models, including black-box systems to some extent, while avoiding data bias, ensuring patient safety, and still complying with legislation, if they can deliver human- comprehensible explanations. Due to skepticism and patient safety reasons, many authors advocate the use of transparent models which may limit their performance and potential. Our study highlights a possible path, by using model explainability, on how to overcome these barriers while allowing the use of more computationally robust models.
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Affiliation(s)
- Mauro F Pinto
- Department of Informatics Engineering, CISUC, University of Coimbra, Coimbra, Portugal
| | - Adriana Leal
- Department of Informatics Engineering, CISUC, University of Coimbra, Coimbra, Portugal
| | - Fábio Lopes
- Department of Informatics Engineering, CISUC, University of Coimbra, Coimbra, Portugal.,Department Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | | | - António Dourado
- Department of Informatics Engineering, CISUC, University of Coimbra, Coimbra, Portugal
| | - Francisco Sales
- Refractory Epilepsy Reference Centre, Centro Hospitalar e Universitário de Coimbra, EPE, Coimbra, Portugal
| | - Pedro Martins
- Department of Informatics Engineering, CISUC, University of Coimbra, Coimbra, Portugal
| | - César A Teixeira
- Department of Informatics Engineering, CISUC, University of Coimbra, Coimbra, Portugal
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21
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Abstract
Metabolomics is the laboratory analysis and scientific study of the metabolome—that is, the entire collection of small molecule chemicals in an organism. The metabolome represents the functional state of an organism and provides a multifaceted readout of the aggregate activity of endogenous (cellular) and exogenous (environmental) processes. In this review, we discuss how the integrative and dynamic properties of the metabolome create unique opportunities to study complex pathologies that evolve and oscillate over time, like epilepsy. We explain how the scientific progress and clinical applications of metabolomics remain hampered by biological and technical challenges, and we propose best practices to overcome these challenges so that metabolomics can be used in a rigorous and effective manner to further epilepsy research.
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Affiliation(s)
- Tore Eid
- Departments of Laboratory Medicine, of Neurosurgery, and of Cellular and Molecular Physiology, Yale School of Medicine, New Haven, CT, USA
- Clinical Chemistry Laboratory, Yale-New Haven Hospital, New Haven, CT, USA
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22
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Cheung F, Pearl PL, Stamoulis C. Novel Seizure Biomarkers in Continuous Electrocardiograms from Pediatric Epilepsy Patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:382-385. [PMID: 34891314 PMCID: PMC8710233 DOI: 10.1109/embc46164.2021.9629760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
There is growing evidence that seizures are accompanied by multi-system changes, not only in the brain but also in organs and systems under its control. Non-EEG measurements from these systems could be leveraged to improve seizure prediction, which is difficult but critical to the success of next-generation epilepsy therapies. Clinical electrophysiology studies during presurgical patient evaluations routinely collect continuous EEG but also ECG data that span multiple days. Prior work has reported electrocardiographic changes but has primarily focused on ventricular activity and brief peri-ictal intervals. Using novel data-driven classification and separation of the ECG high-dimensional signal space, this study investigated seizure-related changes in both ventricular and atrial activity. Measures of complexity as well as heart rate and R-R interval length were analyzed over time in continuous ECGs from 22 pediatric patients with pharmacoresistant seizures and no diagnosed cardiovascular anomalies. Fifteen patients (>68%) had significant changes in atrial or ventricular activity (or both) in intervals containing seizures. Thus, for a substantial number of patients, cardiac markers may be specifically modulated by seizures and could be leveraged to improve and personalize seizure prediction.
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23
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Abstract
SUMMARY Electrical brain stimulation is an established therapy for movement disorders, epilepsy, obsessive compulsive disorder, and a potential therapy for many other neurologic and psychiatric disorders. Despite significant progress and FDA approvals, there remain significant clinical gaps that can be addressed with next generation systems. Integrating wearable sensors and implantable brain devices with off-the-body computing resources (smart phones and cloud resources) opens a new vista for dense behavioral and physiological signal tracking coupled with adaptive stimulation therapy that should have applications for a range of brain and mind disorders. Here, we briefly review some history and current electrical brain stimulation applications for epilepsy, deep brain stimulation and responsive neurostimulation, and emerging applications for next generation devices and systems.
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Affiliation(s)
- Gregory A Worrell
- Department of Neurology, Mayo Bioelectronics and Neurophysiology Laboratory, Mayo Clinic, Rochester, Minnesota, U.S.A
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24
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Rao VR. Chronic electroencephalography in epilepsy with a responsive neurostimulation device: current status and future prospects. Expert Rev Med Devices 2021; 18:1093-1105. [PMID: 34696676 DOI: 10.1080/17434440.2021.1994388] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
INTRODUCTION Implanted neurostimulation devices are gaining traction as therapeutic options for people with certain forms of drug-resistant focal epilepsy. Some of these devices enable chronic electroencephalography (cEEG), which offers views of the dynamics of brain activity in epilepsy over unprecedented time horizons. AREAS COVERED This review focuses on clinical insights and basic neuroscience discoveries enabled by analyses of cEEG from an exemplar device, the NeuroPace RNS® System. Applications of RNS cEEG covered here include counting and lateralizing seizures, quantifying medication response, characterizing spells, forecasting seizures, and exploring mechanisms of cognition. Limitations of the RNS System are discussed in the context of next-generation devices in development. EXPERT OPINION The wide temporal lens of cEEG helps capture the dynamism of epilepsy, revealing phenomena that cannot be appreciated with short duration recordings. The RNS System is a vanguard device whose diagnostic utility rivals its therapeutic benefits, but emerging minimally invasive devices, including those with subscalp recording electrodes, promise to be more applicable within a broad population of people with epilepsy. Epileptology is on the precipice of a paradigm shift in which cEEG is a standard part of diagnostic evaluations and clinical management is predicated on quantitative observations integrated over long timescales.
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Affiliation(s)
- Vikram R Rao
- Associate Professor of Clinical Neurology, Chief, Epilepsy Division, Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
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25
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Grzeskowiak CL, Dumanis SB. Seizure Forecasting: Patient and Caregiver Perspectives. Front Neurol 2021; 12:717428. [PMID: 34616352 PMCID: PMC8488220 DOI: 10.3389/fneur.2021.717428] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Accepted: 08/09/2021] [Indexed: 11/20/2022] Open
Abstract
Accurate seizure forecasting is emerging as a near-term possibility due to recent advancements in machine learning and EEG technology improvements. Large-scale data curation and new data element collection through consumer wearables and digital health tools such as longitudinal seizure diary data has uncovered new possibilities for personalized algorithm development that may be used to predict the likelihood of future seizures. The Epilepsy Foundation recognized the unmet need for development in seizure forecasting following a 2016 survey where an overwhelming majority of respondents across all seizure types and frequencies reported that unpredictability of seizures had the strongest impact on their life while living with or caring for someone living with epilepsy. In early 2021, the Epilepsy Foundation conducted an updated survey among those living with epilepsies and/or their caregivers to better understand the use-cases that best suit the needs of our community as seizure forecast research advances. These results will provide researchers with insight into user-acceptance of using a forecasting tool and incorporation into their daily life. Ultimately, this input from people living with epilepsy and caregivers will provide timely feedback on what the community needs are and ensure researchers and companies first and foremost consider these needs in seizure forecasting tools/product development.
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Affiliation(s)
- Caitlin L. Grzeskowiak
- Epilepsy Foundation of America, Greater Landover, MD, United States
- *Correspondence: Caitlin L. Grzeskowiak
| | - Sonya B. Dumanis
- Epilepsy Foundation of America, Greater Landover, MD, United States
- Coalition for Aligning Science, Chevy Chase, MD, United States
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26
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Shum J, Friedman D. Commercially available seizure detection devices: A systematic review. J Neurol Sci 2021; 428:117611. [PMID: 34419933 DOI: 10.1016/j.jns.2021.117611] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/02/2021] [Accepted: 08/03/2021] [Indexed: 10/20/2022]
Abstract
IMPORTANCE Epilepsy can be associated with significant morbidity and mortality. Seizure detection devices could be invaluable tools for both people with epilepsy, their caregivers, and clinicians as they could alert caretakers about seizures, reduce the risk of sudden unexpected death in epilepsy, and provide objective and more reliable seizure tracking to guide treatment decisions or monitor outcomes in clinical trials. OBJECTIVE To synthesize the characteristics of commercial seizure detection tools/devices currently available. METHODS We performed a systematic search utilizing a diverse set of resources to identify commercially available seizure detection products for consumer use. Performance data was obtained through a systematic review on commercially available products. OBSERVATIONS We identified 23 products marketed for seizure detection/alerting. Devices utilize a variety of mechanisms to detect seizures, including movement detectors, autonomic change detectors, electroencephalogram (EEG) based detectors, and other mechanisms (audio). The optimal device for a person with epilepsy depends on a variety of factors including the main purpose of the device, their age, seizure type and personal preferences. Only 8 devices have published peer-reviewed performance data and the majority for tonic-clonic seizures. An informed conversation between the clinician and the patient can help guide if a seizure detection device is appropriate. CONCLUSIONS AND RELEVANCE Seizure detection devices have a potential to reduce morbidity and mortality for certain people with epilepsy. Clinicians should be familiar with the characteristics of commercially available devices to best counsel their patients on whether a seizure detection device may be beneficial and what the optimal devices may be.
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Affiliation(s)
- Jennifer Shum
- Department of Neurology, Comprehensive Epilepsy Center, New York University Gross School of Medicine, New York, NY, USA.
| | - Daniel Friedman
- Department of Neurology, Comprehensive Epilepsy Center, New York University Gross School of Medicine, New York, NY, USA
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27
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Stirling RE, Maturana MI, Karoly PJ, Nurse ES, McCutcheon K, Grayden DB, Ringo SG, Heasman JM, Hoare RJ, Lai A, D'Souza W, Seneviratne U, Seiderer L, McLean KJ, Bulluss KJ, Murphy M, Brinkmann BH, Richardson MP, Freestone DR, Cook MJ. Seizure Forecasting Using a Novel Sub-Scalp Ultra-Long Term EEG Monitoring System. Front Neurol 2021; 12:713794. [PMID: 34497578 PMCID: PMC8419461 DOI: 10.3389/fneur.2021.713794] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 07/27/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate identification of seizure activity, both clinical and subclinical, has important implications in the management of epilepsy. Accurate recognition of seizure activity is essential for diagnostic, management and forecasting purposes, but patient-reported seizures have been shown to be unreliable. Earlier work has revealed accurate capture of electrographic seizures and forecasting is possible with an implantable intracranial device, but less invasive electroencephalography (EEG) recording systems would be optimal. Here, we present preliminary results of seizure detection and forecasting with a minimally invasive sub-scalp device that continuously records EEG. Five participants with refractory epilepsy who experience at least two clinically identifiable seizures monthly have been implanted with sub-scalp devices (Minder®), providing two channels of data from both hemispheres of the brain. Data is continuously captured via a behind-the-ear system, which also powers the device, and transferred wirelessly to a mobile phone, from where it is accessible remotely via cloud storage. EEG recordings from the sub-scalp device were compared to data recorded from a conventional system during a 1-week ambulatory video-EEG monitoring session. Suspect epileptiform activity (EA) was detected using machine learning algorithms and reviewed by trained neurophysiologists. Seizure forecasting was demonstrated retrospectively by utilizing cycles in EA and previous seizure times. The procedures and devices were well-tolerated and no significant complications have been reported. Seizures were accurately identified on the sub-scalp system, as visually confirmed by periods of concurrent conventional scalp EEG recordings. The data acquired also allowed seizure forecasting to be successfully undertaken. The area under the receiver operating characteristic curve (AUC score) achieved (0.88), which is comparable to the best score in recent, state-of-the-art forecasting work using intracranial EEG.
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Affiliation(s)
- Rachel E. Stirling
- Seer Medical Pty Ltd, Melbourne, VIC, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Matias I. Maturana
- Seer Medical Pty Ltd, Melbourne, VIC, Australia
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
| | - Philippa J. Karoly
- Seer Medical Pty Ltd, Melbourne, VIC, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Ewan S. Nurse
- Seer Medical Pty Ltd, Melbourne, VIC, Australia
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
| | | | - David B. Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
| | | | - John M. Heasman
- Epi-Minder Pty. Ltd., Melbourne, VIC, Australia
- Cochlear Limited, Sydney, NSW, Australia
| | | | - Alan Lai
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Neuroscience, St. Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Wendyl D'Souza
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Neuroscience, St. Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Udaya Seneviratne
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Neuroscience, Monash Medical Centre, Melbourne, VIC, Australia
- Department of Medicine, School of Clinical Sciences at Monash Health, Monash University, Melbourne, VIC, Australia
| | - Linda Seiderer
- Department of Neuroscience, St. Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Karen J. McLean
- Epi-Minder Pty. Ltd., Melbourne, VIC, Australia
- Department of Neuroscience, St. Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Kristian J. Bulluss
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Neuroscience, St. Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Michael Murphy
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Department of Neuroscience, St. Vincent's Hospital Melbourne, Fitzroy, VIC, Australia
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Lab, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Mark P. Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | | | - Mark J. Cook
- Seer Medical Pty Ltd, Melbourne, VIC, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- Department of Medicine at St. Vincent's Hospital Melbourne, The University of Melbourne, Fitzroy, VIC, Australia
- Epi-Minder Pty. Ltd., Melbourne, VIC, Australia
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28
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Stirling RE, Grayden DB, D'Souza W, Cook MJ, Nurse E, Freestone DR, Payne DE, Brinkmann BH, Pal Attia T, Viana PF, Richardson MP, Karoly PJ. Forecasting Seizure Likelihood With Wearable Technology. Front Neurol 2021; 12:704060. [PMID: 34335457 PMCID: PMC8320020 DOI: 10.3389/fneur.2021.704060] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Accepted: 06/17/2021] [Indexed: 12/11/2022] Open
Abstract
The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated with seizures but need to be feasible and accessible in the long-term. Wearable devices are perfect candidates to develop non-invasive, accessible forecasts but are yet to be investigated in long-term studies. We hypothesized that machine learning models could utilize heart rate as a biomarker for well-established cycles of seizures and epileptic activity, in addition to other wearable signals, to forecast high and low risk seizure periods. This feasibility study tracked participants' (n = 11) heart rates, sleep, and step counts using wearable smartwatches and seizure occurrence using smartphone seizure diaries for at least 6 months (mean = 14.6 months, SD = 3.8 months). Eligible participants had a diagnosis of refractory epilepsy and reported at least 20 seizures (mean = 135, SD = 123) during the recording period. An ensembled machine learning and neural network model estimated seizure risk either daily or hourly, with retraining occurring on a weekly basis as additional data was collected. Performance was evaluated retrospectively against a rate-matched random forecast using the area under the receiver operating curve. A pseudo-prospective evaluation was also conducted on a held-out dataset. Of the 11 participants, seizures were predicted above chance in all (100%) participants using an hourly forecast and in ten (91%) participants using a daily forecast. The average time spent in high risk (prediction time) before a seizure occurred was 37 min in the hourly forecast and 3 days in the daily forecast. Cyclic features added the most predictive value to the forecasts, particularly circadian and multiday heart rate cycles. Wearable devices can be used to produce patient-specific seizure forecasts, particularly when biomarkers of seizure and epileptic activity cycles are utilized.
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Affiliation(s)
- Rachel E. Stirling
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - David B. Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- Departments of Medicine and Neurology, St Vincent's Hospital, The University of Melbourne, Melbourne, VIC, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Wendyl D'Souza
- Departments of Medicine and Neurology, St Vincent's Hospital, The University of Melbourne, Melbourne, VIC, Australia
| | - Mark J. Cook
- Departments of Medicine and Neurology, St Vincent's Hospital, The University of Melbourne, Melbourne, VIC, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Ewan Nurse
- Departments of Medicine and Neurology, St Vincent's Hospital, The University of Melbourne, Melbourne, VIC, Australia
- Seer Medical, Melbourne, VIC, Australia
| | | | - Daniel E. Payne
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
| | - Benjamin H. Brinkmann
- Bioelectronics Neurophysiology and Engineering Lab, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Tal Pal Attia
- Bioelectronics Neurophysiology and Engineering Lab, Department of Neurology, Mayo Clinic, Rochester, MN, United States
| | - Pedro F. Viana
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Mark P. Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Philippa J. Karoly
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
- Departments of Medicine and Neurology, St Vincent's Hospital, The University of Melbourne, Melbourne, VIC, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, VIC, Australia
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29
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Brinkmann BH, Karoly PJ, Nurse ES, Dumanis SB, Nasseri M, Viana PF, Schulze-Bonhage A, Freestone DR, Worrell G, Richardson MP, Cook MJ. Seizure Diaries and Forecasting With Wearables: Epilepsy Monitoring Outside the Clinic. Front Neurol 2021; 12:690404. [PMID: 34326807 PMCID: PMC8315760 DOI: 10.3389/fneur.2021.690404] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/10/2021] [Indexed: 12/14/2022] Open
Abstract
It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic–clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.
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Affiliation(s)
| | - Philippa J Karoly
- Department of Medicine, Graeme Clark Institute and St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
| | - Ewan S Nurse
- Department of Medicine, Graeme Clark Institute and St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia.,Seer Medical, Melbourne, VIC, Australia
| | | | - Mona Nasseri
- Department of Neurology, Mayo Foundation, Rochester, MN, United States.,School of Engineering, University of North Florida, Jacksonville, FL, United States
| | - Pedro F Viana
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Faculty of Medicine, University of Lisbon, Lisboa, Portugal
| | - Andreas Schulze-Bonhage
- Faculty of Medicine, Epilepsy Center, Medical Center, University of Freiburg, Freiburg, Germany
| | | | - Greg Worrell
- Department of Neurology, Mayo Foundation, Rochester, MN, United States
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Mark J Cook
- Department of Medicine, Graeme Clark Institute and St Vincent's Hospital, The University of Melbourne, Fitzroy, VIC, Australia
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30
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Bosl WJ, Leviton A, Loddenkemper T. Prediction of Seizure Recurrence. A Note of Caution. Front Neurol 2021; 12:675728. [PMID: 34054713 PMCID: PMC8155381 DOI: 10.3389/fneur.2021.675728] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 04/20/2021] [Indexed: 12/31/2022] Open
Abstract
Great strides have been made recently in documenting that machine-learning programs can predict seizure occurrence in people who have epilepsy. Along with this progress have come claims that appear to us to be a bit premature. We anticipate that many people will benefit from seizure prediction. We also doubt that all will benefit. Although machine learning is a useful tool for aiding discovery, we believe that the greatest progress will come from deeper understanding of seizures, epilepsy, and the EEG features that enable seizure prediction. In this essay, we lay out reasons for optimism and skepticism.
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Affiliation(s)
- William J Bosl
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States.,Health Informatics Program, University of San Francisco, San Francisco, CA, United States
| | - Alan Leviton
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
| | - Tobias Loddenkemper
- Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Boston, MA, United States
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31
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Karoly PJ, Rao VR, Gregg NM, Worrell GA, Bernard C, Cook MJ, Baud MO. Cycles in epilepsy. Nat Rev Neurol 2021; 17:267-284. [PMID: 33723459 DOI: 10.1038/s41582-021-00464-1] [Citation(s) in RCA: 114] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/19/2021] [Indexed: 01/31/2023]
Abstract
Epilepsy is among the most dynamic disorders in neurology. A canonical view holds that seizures, the characteristic sign of epilepsy, occur at random, but, for centuries, humans have looked for patterns of temporal organization in seizure occurrence. Observations that seizures are cyclical date back to antiquity, but recent technological advances have, for the first time, enabled cycles of seizure occurrence to be quantitatively characterized with direct brain recordings. Chronic recordings of brain activity in humans and in animals have yielded converging evidence for the existence of cycles of epileptic brain activity that operate over diverse timescales: daily (circadian), multi-day (multidien) and yearly (circannual). Here, we review this evidence, synthesizing data from historical observational studies, modern implanted devices, electronic seizure diaries and laboratory-based animal neurophysiology. We discuss advances in our understanding of the mechanistic underpinnings of these cycles and highlight the knowledge gaps that remain. The potential clinical applications of a knowledge of cycles in epilepsy, including seizure forecasting and chronotherapy, are discussed in the context of the emerging concept of seizure risk. In essence, this Review addresses the broad question of why seizures occur when they occur.
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Affiliation(s)
- Philippa J Karoly
- Graeme Clark Institute, The University of Melbourne, Melbourne, Victoria, Australia.
| | - Vikram R Rao
- Department of Neurology, University of California, San Francisco, CA, USA.,Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Nicholas M Gregg
- Bioelectronics, Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Gregory A Worrell
- Bioelectronics, Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - Christophe Bernard
- Aix Marseille University, Inserm, Institut de Neurosciences des Systèmes, Marseille, France
| | - Mark J Cook
- Graeme Clark Institute, The University of Melbourne, Melbourne, Victoria, Australia
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland. .,Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland.
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32
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A digital nervous system aiming toward personalized IoT healthcare. Sci Rep 2021; 11:7757. [PMID: 33833303 PMCID: PMC8032817 DOI: 10.1038/s41598-021-87177-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 03/24/2021] [Indexed: 12/26/2022] Open
Abstract
Body area networks (BANs), cloud computing, and machine learning are platforms that can potentially enable advanced healthcare outside the hospital. By applying distributed sensors and drug delivery devices on/in our body and connecting to such communication and decision-making technology, a system for remote diagnostics and therapy is achieved with additional autoregulation capabilities. Challenges with such autarchic on-body healthcare schemes relate to integrity and safety, and interfacing and transduction of electronic signals into biochemical signals, and vice versa. Here, we report a BAN, comprising flexible on-body organic bioelectronic sensors and actuators utilizing two parallel pathways for communication and decision-making. Data, recorded from strain sensors detecting body motion, are both securely transferred to the cloud for machine learning and improved decision-making, and sent through the body using a secure body-coupled communication protocol to auto-actuate delivery of neurotransmitters, all within seconds. We conclude that both highly stable and accurate sensing-from multiple sensors-are needed to enable robust decision making and limit the frequency of retraining. The holistic platform resembles the self-regulatory properties of the nervous system, i.e., the ability to sense, communicate, decide, and react accordingly, thus operating as a digital nervous system.
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33
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Chiang S, Moss R, Black AP, Jackson M, Moss C, Bidwell J, Meisel C, Loddenkemper T. Evaluation and recommendations for effective data visualization for seizure forecasting algorithms. JAMIA Open 2021; 4:ooab009. [PMID: 33709064 PMCID: PMC7935496 DOI: 10.1093/jamiaopen/ooab009] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 02/05/2021] [Indexed: 12/15/2022] Open
Abstract
Objective Seizure forecasting algorithms have become increasingly accurate and may reduce the morbidity and mortality caused by seizure unpredictability. Translating these benefits into meaningful health outcomes for people with epilepsy requires effective data visualization of algorithm outputs. To date, no studies have investigated patient and physician perspectives on effective translation of algorithm outputs into data visualizations through health information technology. Materials and Methods We developed front-end data visualizations as part of a Seizure Forecast Visualization Toolkit. We surveyed 627 people living with epilepsy and caregivers, and 28 epilepsy healthcare providers. Respondents scored each visualization in terms of international standardized software quality criteria for functionality, appropriateness, and usability. Results People with epilepsy and caregivers ranked hourly radar charts highest for protecting against errors in interpreting forecasts, reducing anxiety from seizure unpredictability, and understanding seizure patterns. Accuracy in interpreting visuals, such as a risk gauge, was dependent on seizure frequency. Visuals showing hourly/daily forecasts were more useful for patients who experienced seizure cycling than those who did not. Hourly line graphs and monthly heat maps were rated highest among clinicians for ease of understanding, anticipated integration into clinical practice, and the likelihood of clinical usage. Epilepsy providers indicated that daily heat maps, daily line graphs, and hourly line graphs were most useful for interpreting seizure diary patterns, assessing therapy impact, and counseling on seizure safety. Discussion The choice of data visualization impacts the effective translation of seizure forecast algorithms into meaningful health outcomes. Conclusion This effort underlines the importance of incorporating standardized, quantitative methods for assessing the effectiveness of data visualization to translate seizure forecast algorithms into clinical practice.
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Affiliation(s)
- Sharon Chiang
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Robert Moss
- Seizure Tracker, LLC, Springfield, Virginia, USA
| | - Angela P Black
- Department of Pediatrics, Corridor Primary Care, San Marcos, Texas, USA
| | - Michele Jackson
- Divison of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Chuck Moss
- Seizure Tracker, LLC, Springfield, Virginia, USA
| | - Jonathan Bidwell
- Divison of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Christian Meisel
- Department of Neurology, Charité - Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany
| | - Tobias Loddenkemper
- Divison of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Rasheed K, Qayyum A, Qadir J, Sivathamboo S, Kwan P, Kuhlmann L, O'Brien T, Razi A. Machine Learning for Predicting Epileptic Seizures Using EEG Signals: A Review. IEEE Rev Biomed Eng 2021; 14:139-155. [PMID: 32746369 DOI: 10.1109/rbme.2020.3008792] [Citation(s) in RCA: 78] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
With the advancement in artificial intelligence (AI) and machine learning (ML) techniques, researchers are striving towards employing these techniques for advancing clinical practice. One of the key objectives in healthcare is the early detection and prediction of disease to timely provide preventive interventions. This is especially the case for epilepsy, which is characterized by recurrent and unpredictable seizures. Patients can be relieved from the adverse consequences of epileptic seizures if it could somehow be predicted in advance. Despite decades of research, seizure prediction remains an unsolved problem. This is likely to remain at least partly because of the inadequate amount of data to resolve the problem. There have been exciting new developments in ML-based algorithms that have the potential to deliver a paradigm shift in the early and accurate prediction of epileptic seizures. Here we provide a comprehensive review of state-of-the-art ML techniques in early prediction of seizures using EEG signals. We will identify the gaps, challenges, and pitfalls in the current research and recommend future directions.
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Payne DE, Dell KL, Karoly PJ, Kremen V, Gerla V, Kuhlmann L, Worrell GA, Cook MJ, Grayden DB, Freestone DR. Identifying seizure risk factors: A comparison of sleep, weather, and temporal features using a Bayesian forecast. Epilepsia 2020; 62:371-382. [PMID: 33377501 DOI: 10.1111/epi.16785] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 11/15/2020] [Accepted: 11/16/2020] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Most seizure forecasting algorithms have relied on features specific to electroencephalographic recordings. Environmental and physiological factors, such as weather and sleep, have long been suspected to affect brain activity and seizure occurrence but have not been fully explored as prior information for seizure forecasts in a patient-specific analysis. The study aimed to quantify whether sleep, weather, and temporal factors (time of day, day of week, and lunar phase) can provide predictive prior probabilities that may be used to improve seizure forecasts. METHODS This study performed post hoc analysis on data from eight patients with a total of 12.2 years of continuous intracranial electroencephalographic recordings (average = 1.5 years, range = 1.0-2.1 years), originally collected in a prospective trial. Patients also had sleep scoring and location-specific weather data. Histograms of future seizure likelihood were generated for each feature. The predictive utility of individual features was measured using a Bayesian approach to combine different features into an overall forecast of seizure likelihood. Performance of different feature combinations was compared using the area under the receiver operating curve. Performance evaluation was pseudoprospective. RESULTS For the eight patients studied, seizures could be predicted above chance accuracy using sleep (five patients), weather (two patients), and temporal features (six patients). Forecasts using combined features performed significantly better than chance in six patients. For four of these patients, combined forecasts outperformed any individual feature. SIGNIFICANCE Environmental and physiological data, including sleep, weather, and temporal features, provide significant predictive information on upcoming seizures. Although forecasts did not perform as well as algorithms that use invasive intracranial electroencephalography, the results were significantly above chance. Complementary signal features derived from an individual's historic seizure records may provide useful prior information to augment traditional seizure detection or forecasting algorithms. Importantly, many predictive features used in this study can be measured noninvasively.
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Affiliation(s)
- Daniel E Payne
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Katrina L Dell
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Phillipa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Victoria, Australia
| | - Vaclav Kremen
- Department of Neurology, Mayo Clinic, Rochester, MN, USA.,Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Vaclav Gerla
- Czech Institute of Informatics, Robotics, and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Levin Kuhlmann
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Data Science and AI, Faculty of IT, Monash University, Clayton, Victoria, Australia
| | | | - Mark J Cook
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Victoria, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
| | - Dean R Freestone
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Victoria, Australia
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Proix T, Truccolo W, Leguia MG, Tcheng TK, King-Stephens D, Rao VR, Baud MO. Forecasting seizure risk in adults with focal epilepsy: a development and validation study. Lancet Neurol 2020; 20:127-135. [PMID: 33341149 DOI: 10.1016/s1474-4422(20)30396-3] [Citation(s) in RCA: 81] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 10/09/2020] [Accepted: 10/14/2020] [Indexed: 01/12/2023]
Abstract
BACKGROUND People with epilepsy are burdened with the apparent unpredictability of seizures. In the past decade, converging evidence from studies using chronic EEG (cEEG) revealed that epileptic brain activity shows robust cycles, operating over hours (circadian) and days (multidien). We hypothesised that these cycles can be leveraged to estimate future seizure probability, and we tested the feasibility of forecasting seizures days in advance. METHODS We did a feasibility study in distinct development and validation cohorts, involving retrospective analysis of cEEG data recorded with an implanted device in adults (age ≥18 years) with drug-resistant focal epilepsy followed at 35 centres across the USA between Jan 19, 2004, and May 18, 2018. Patients were required to have had 20 or more electrographic seizures (development cohort) or self-reported seizures (validation cohort). In all patients, the device recorded interictal epileptiform activity (IEA; ≥6 months of continuous hourly data), the fluctuations in which helped estimate varying seizure risk. Point process statistical models trained on initial portions of each patient's cEEG data (both cohorts) generated forecasts of seizure probability that were tested on subsequent unseen seizure data and evaluated against surrogate time-series. The primary outcome was the percentage of patients with forecasts showing improvement over chance (IoC). FINDINGS We screened 72 and 256 patients, and included 18 and 157 patients in the development and validation cohorts, respectively. Models incorporating information about multidien IEA cycles alone generated daily seizure forecasts for the next calendar day with IoC in 15 (83%) patients in the development cohort and 103 (66%) patients in the validation cohort. The forecasting horizon could be extended up to 3 days while maintaining IoC in two (11%) of 18 patients and 61 (39%) of 157 patients. Forecasts with a shorter horizon of 1 h, possible only for electrographic seizures in the development cohort, showed IoC in all 18 (100%) patients. INTERPRETATION This study shows that seizure probability can be forecasted days in advance by leveraging multidien IEA cycles recorded with an implanted device. This study will serve as a basis for prospective clinical trials to establish how people with epilepsy might benefit from seizure forecasting over long horizons. FUNDING None. VIDEO ABSTRACT.
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Affiliation(s)
- Timothée Proix
- Department of Basic Neurosciences, Faculty of Medicine, University of Geneva, Geneva, Switzerland; Department of Neuroscience, Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Wilson Truccolo
- Department of Neuroscience, Carney Institute for Brain Science, Brown University, Providence, RI, USA
| | - Marc G Leguia
- Sleep-Wake-Epilepsy Center, NeuroTec and Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland
| | | | | | - Vikram R Rao
- Department of Neurology and Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Maxime O Baud
- Sleep-Wake-Epilepsy Center, NeuroTec and Center for Experimental Neurology, Department of Neurology, Inselspital Bern, University Hospital, University of Bern, Bern, Switzerland; Wyss Center for Bio and Neuroengineering, Geneva, Switzerland.
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Viana PF, Duun-Henriksen J, Glasstëter M, Dümpelmann M, Nurse ES, Martins IP, Dumanis SB, Schulze-Bonhage A, Freestone DR, Brinkmann BH, Richardson MP. 230 days of ultra long-term subcutaneous EEG: seizure cycle analysis and comparison to patient diary. Ann Clin Transl Neurol 2020; 8:288-293. [PMID: 33275838 PMCID: PMC7818131 DOI: 10.1002/acn3.51261] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/23/2020] [Accepted: 11/10/2020] [Indexed: 12/19/2022] Open
Abstract
We describe the longest period of subcutaneous EEG (sqEEG) monitoring to date, in a 35‐year‐old female with refractory epilepsy. Over 230 days, 4791/5520 h of sqEEG were recorded (86%, mean 20.8 [IQR 3.9] hours/day). Using an electronic diary, the patient reported 22 seizures, while automatically‐assisted visual sqEEG review detected 32 seizures. There was substantial agreement between days of reported and recorded seizures (Cohen’s kappa 0.664), although multiple clustered seizures remained undocumented. Circular statistics identified significant sqEEG seizure cycles at circadian (24‐hour) and multidien (5‐day) timescales. Electrographic seizure monitoring and analysis of long‐term seizure cycles are possible with this neurophysiological tool.
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Affiliation(s)
- Pedro F Viana
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.,Faculty of Medicine, University of Lisbon, Lisboa, Portugal
| | | | - Martin Glasstëter
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Ewan S Nurse
- Seer Medical Inc., Melbourne, Victoria, Australia.,Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | | | | | - Andreas Schulze-Bonhage
- Epilepsy Center, Department for Neurosurgery, University Medical Center Freiburg, Freiburg, Germany
| | - Dean R Freestone
- Seer Medical Inc., Melbourne, Victoria, Australia.,Department of Medicine, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Benjamin H Brinkmann
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
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Meisel C, El Atrache R, Jackson M, Schubach S, Ufongene C, Loddenkemper T. Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting. Epilepsia 2020; 61:2653-2666. [PMID: 33040327 DOI: 10.1111/epi.16719] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 09/17/2020] [Accepted: 09/17/2020] [Indexed: 12/12/2022]
Abstract
OBJECTIVE Seizure forecasting may provide patients with timely warnings to adapt their daily activities and help clinicians deliver more objective, personalized treatments. Although recent work has convincingly demonstrated that seizure risk assessment is in principle possible, these early approaches relied largely on complex, often invasive setups including intracranial electrocorticography, implanted devices, and multichannel electroencephalography, and required patient-specific adaptation or learning to perform optimally, all of which limit translation to broad clinical application. To facilitate broader adaptation of seizure forecasting in clinical practice, noninvasive, easily applicable techniques that reliably assess seizure risk without much prior tuning are crucial. Wristbands that continuously record physiological parameters, including electrodermal activity, body temperature, blood volume pulse, and actigraphy, may afford monitoring of autonomous nervous system function and movement relevant for such a task, hence minimizing potential complications associated with invasive monitoring and avoiding stigma associated with bulky external monitoring devices on the head. METHODS Here, we applied deep learning on multimodal wristband sensor data from 69 patients with epilepsy (total duration > 2311 hours, 452 seizures) to assess its capability to forecast seizures in a statistically significant way. RESULTS Using a leave-one-subject-out cross-validation approach, we identified better-than-chance predictability in 43% of the patients. Time-matched seizure surrogate data analyses indicated forecasting not to be driven simply by time of day or vigilance state. Prediction performance peaked when all sensor modalities were used, and did not differ between generalized and focal seizure types, but generally increased with the size of the training dataset, indicating potential further improvement with larger datasets in the future. SIGNIFICANCE Collectively, these results show that statistically significant seizure risk assessments are feasible from easy-to-use, noninvasive wearable devices without the need of patient-specific training or parameter optimization.
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Affiliation(s)
- Christian Meisel
- Department of Neurology, Charité-Universitätsmedizin Berlin, Berlin, Germany.,Berlin Institute of Health, Berlin, Germany.,Boston Children's Hospital, Boston, MA, USA
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Beniczky S, Karoly P, Nurse E, Ryvlin P, Cook M. Machine learning and wearable devices of the future. Epilepsia 2020; 62 Suppl 2:S116-S124. [PMID: 32712958 DOI: 10.1111/epi.16555] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Revised: 05/05/2020] [Accepted: 05/08/2020] [Indexed: 01/06/2023]
Abstract
Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non-EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Philippa Karoly
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
| | - Ewan Nurse
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, CHUV, Lausanne, Switzerland
| | - Mark Cook
- The Graeme Clark Institute, The University of Melbourne, Melbourne, Vic., Australia
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40
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Stirling RE, Cook MJ, Grayden DB, Karoly PJ. Seizure forecasting and cyclic control of seizures. Epilepsia 2020; 62 Suppl 1:S2-S14. [PMID: 32712968 DOI: 10.1111/epi.16541] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 04/23/2020] [Accepted: 04/27/2020] [Indexed: 02/02/2023]
Abstract
Epilepsy is a unique neurologic condition characterized by recurrent seizures, where causes, underlying biomarkers, triggers, and patterns differ across individuals. The unpredictability of seizures can heighten fear and anxiety in people with epilepsy, making it difficult to take part in day-to-day activities. Epilepsy researchers have prioritized developing seizure prediction algorithms to combat episodic seizures for decades, but the utility and effectiveness of prediction algorithms has not been investigated thoroughly in clinical settings. In contrast, seizure forecasts, which theoretically provide the probability of a seizure at any time (as opposed to predicting the next seizure occurrence), may be more feasible. Many advances have been made over the past decade in the field of seizure forecasting, including improvements in algorithms as a result of machine learning and exploration of non-EEG-based measures of seizure susceptibility, such as physiological biomarkers, behavioral changes, environmental drivers, and cyclic seizure patterns. For example, recent work investigating periodicities in individual seizure patterns has determined that more than 90% of people have circadian rhythms in their seizures, and many also experience multiday, weekly, or longer cycles. Other potential indicators of seizure susceptibility include stress levels, heart rate, and sleep quality, all of which have the potential to be captured noninvasively over long time scales. There are many possible applications of a seizure-forecasting device, including improving quality of life for people with epilepsy, guiding treatment plans and medication titration, optimizing presurgical monitoring, and focusing scientific research. To realize this potential, it is vital to better understand the user requirements of a seizure-forecasting device, continue to advance forecasting algorithms, and design clear guidelines for prospective clinical trials of seizure forecasting.
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Affiliation(s)
- Rachel E Stirling
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia
| | - Mark J Cook
- Graeme Clark Institute & St Vincent's Hospital, The University of Melbourne, Melbourne, Vic., Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia
| | - Philippa J Karoly
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Vic., Australia.,Graeme Clark Institute & St Vincent's Hospital, The University of Melbourne, Melbourne, Vic., Australia
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Vieluf S, Reinsberger C, El Atrache R, Jackson M, Schubach S, Ufongene C, Loddenkemper T, Meisel C. Autonomic nervous system changes detected with peripheral sensors in the setting of epileptic seizures. Sci Rep 2020; 10:11560. [PMID: 32665704 PMCID: PMC7360606 DOI: 10.1038/s41598-020-68434-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Accepted: 06/15/2020] [Indexed: 11/09/2022] Open
Abstract
A better understanding of the early detection of seizures is highly desirable as identification of an impending seizure may afford improved treatments, such as antiepileptic drug chronotherapy, or timely warning to patients. While epileptic seizures are known to often manifest also with autonomic nervous system (ANS) changes, it is not clear whether ANS markers, if recorded from a wearable device, are also informative about an impending seizure with statistically significant sensitivity and specificity. Using statistical testing with seizure surrogate data and a unique dataset of continuously recorded multi-day wristband data including electrodermal activity (EDA), temperature (TEMP) and heart rate (HR) from 66 people with epilepsy (9.9 ± 5.8 years; 27 females; 161 seizures) we investigated differences between inter- and preictal periods in terms of mean, variance, and entropy of these signals. We found that signal mean and variance do not differentiate between inter- and preictal periods in a statistically meaningful way. EDA signal entropy was found to be increased prior to seizures in a small subset of patients. Findings may provide novel insights into the pathophysiology of epileptic seizures with respect to ANS function, and, while further validation and investigation of potential causes of the observed changes are needed, indicate that epilepsy-related state changes may be detectable using peripheral wearable devices. Detection of such changes with wearable devices may be more feasible for everyday monitoring than utilizing an electroencephalogram.
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Affiliation(s)
- Solveig Vieluf
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA.,Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
| | - Claus Reinsberger
- Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany.,Edward E. Bromfield Epilepsy Center, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA, 02115, USA
| | - Rima El Atrache
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
| | - Michele Jackson
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
| | - Sarah Schubach
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
| | - Claire Ufongene
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
| | - Tobias Loddenkemper
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA
| | - Christian Meisel
- Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA, 02115, USA. .,Department of Neurology, University Clinic Carl Gustav Carus, Fetscherstraße 74, Dresden, 01307, Germany.
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Goldenholz DM, Goldenholz SR, Romero J, Moss R, Sun H, Westover B. Development and Validation of Forecasting Next Reported Seizure Using e-Diaries. Ann Neurol 2020; 88:588-595. [PMID: 32567720 DOI: 10.1002/ana.25812] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 06/05/2020] [Accepted: 06/08/2020] [Indexed: 12/23/2022]
Abstract
OBJECTIVE There are no validated methods for predicting the timing of seizures. Using machine learning, we sought to forecast 24-hour risk of self-reported seizure from e-diaries. METHODS Data from 5,419 patients on SeizureTracker.com (including seizure count, type, and duration) were split into training (3,806 patients/1,665,215 patient-days) and testing (1,613 patients/549,588 patient-days) sets with no overlapping patients. An artificial intelligence (AI) program, consisting of recurrent networks followed by a multilayer perceptron ("deep learning" model), was trained to produce risk forecasts. Forecasts were made from a sliding window of 3-month diary history for each day of each patient's diary. After training, the model parameters were held constant and the testing set was scored. A rate-matched random (RMR) forecast was compared to the AI. Comparisons were made using the area under the receiver operating characteristic curve (AUC), a measure of binary discrimination performance, and the Brier score, a measure of forecast calibration. The Brier skill score (BSS) measured the improvement of the AI Brier score compared to the benchmark RMR Brier score. Confidence intervals (CIs) on performance statistics were obtained via bootstrapping. RESULTS The AUC was 0.86 (95% CI = 0.85-0.88) for AI and 0.83 (95% CI = 0.81-0.85) for RMR, favoring AI (p < 0.001). Overall (all patients combined), BSS was 0.27 (95% CI = 0.23-0.31), also favoring AI (p < 0.001). INTERPRETATION The AI produced a valid forecast superior to a chance forecaster, and provided meaningful forecasts in the majority of patients. Future studies will be needed to quantify the clinical value of these forecasts for patients. ANN NEUROL 2020;88:588-595.
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Affiliation(s)
- Daniel M Goldenholz
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Shira R Goldenholz
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Juan Romero
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA.,Harvard Medical School, Boston, Massachusetts, USA
| | - Rob Moss
- Seizure Tracker, Springfield, Virginia, USA
| | - Haoqi Sun
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Brandon Westover
- Harvard Medical School, Boston, Massachusetts, USA.,Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
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Nasseri M, Nurse E, Glasstetter M, Böttcher S, Gregg NM, Laks Nandakumar A, Joseph B, Pal Attia T, Viana PF, Bruno E, Biondi A, Cook M, Worrell GA, Schulze-Bonhage A, Dümpelmann M, Freestone DR, Richardson MP, Brinkmann BH. Signal quality and patient experience with wearable devices for epilepsy management. Epilepsia 2020; 61 Suppl 1:S25-S35. [PMID: 32497269 DOI: 10.1111/epi.16527] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2020] [Revised: 04/14/2020] [Accepted: 04/14/2020] [Indexed: 01/24/2023]
Abstract
Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor-quality or corrupt data segments. In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in-hospital or in-home electroencephalographic (EEG) monitoring, and healthy volunteers. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high-quality, marginal-quality, or poor-quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good-quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good-quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good-, marginal-, and poor-quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist-worn devices, and the Empatica E4 device was preferred most often. Current wearable devices can provide high-quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.
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Affiliation(s)
- Mona Nasseri
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Ewan Nurse
- Seer Medical, Melbourne, Victoria, Australia.,Department of Medicine, St Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Martin Glasstetter
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Sebastian Böttcher
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Nicholas M Gregg
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Boney Joseph
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Tal Pal Attia
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Pedro F Viana
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK.,Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Elisa Bruno
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Andrea Biondi
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Mark Cook
- Department of Medicine, St Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Gregory A Worrell
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Andreas Schulze-Bonhage
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Matthias Dümpelmann
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | | | - Mark P Richardson
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Benjamin H Brinkmann
- Bioelectronics Neurophysiology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
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Maturana MI, Meisel C, Dell K, Karoly PJ, D'Souza W, Grayden DB, Burkitt AN, Jiruska P, Kudlacek J, Hlinka J, Cook MJ, Kuhlmann L, Freestone DR. Critical slowing down as a biomarker for seizure susceptibility. Nat Commun 2020; 11:2172. [PMID: 32358560 PMCID: PMC7195436 DOI: 10.1038/s41467-020-15908-3] [Citation(s) in RCA: 83] [Impact Index Per Article: 20.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Accepted: 03/30/2020] [Indexed: 02/04/2023] Open
Abstract
The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system suggest that prior to a seizure, recorded brain signals may exhibit critical slowing down, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored key signatures of critical slowing down prior to seizures. The metrics used to detect critical slowing down fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing down is a reliable indicator that could be used in seizure forecasting algorithms. Critical slowing (associated with increased variance and autocorrelation) can precede critical state transitions. Here, the authors show critical slowing can be used as a marker in seizure forecasting algorithms.
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Affiliation(s)
- Matias I Maturana
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia. .,Seer Medical, Melbourne, Australia.
| | - Christian Meisel
- Department of Neurology, University Clinic Carl Gustav Carus, Dresden, Germany.,Boston Children's Hospital, Boston, MA, USA
| | - Katrina Dell
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia
| | - Philippa J Karoly
- Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Wendyl D'Souza
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia
| | - David B Grayden
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Anthony N Burkitt
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Australia
| | - Premysl Jiruska
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic.,Department of Developmental Epileptology, Institute of Physiology, Czech Academy of Sciences, Prague, Czech Republic
| | - Jan Kudlacek
- Department of Physiology, Second Faculty of Medicine, Charles University, Prague, Czech Republic.,Department of Developmental Epileptology, Institute of Physiology, Czech Academy of Sciences, Prague, Czech Republic.,Department of Circuit Theory, Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic.,National Institute of Mental Health, Klecany, Czech Republic
| | - Mark J Cook
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Graeme Clark Institute, The University of Melbourne, Melbourne, Australia
| | - Levin Kuhlmann
- Department of Medicine, St Vincent's Hospital, The University of Melbourne, Melbourne, Australia.,Faculty of Information Technology, Monash University, Clayton, Victoria, Australia.,Centre for Human Psychopharmacology, Swinburne University of Technology, Hawthorn, Victoria, Australia
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45
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Hixson JD, Braverman L. Digital tools for epilepsy: Opportunities and barriers. Epilepsy Res 2020; 162:106233. [DOI: 10.1016/j.eplepsyres.2019.106233] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Revised: 09/10/2019] [Accepted: 10/26/2019] [Indexed: 11/27/2022]
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46
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Karoly PJ, Cook MJ, Maturana M, Nurse ES, Payne D, Brinkmann BH, Grayden DB, Dumanis SB, Richardson MP, Worrell GA, Schulze‐Bonhage A, Kuhlmann L, Freestone DR. Forecasting cycles of seizure likelihood. Epilepsia 2020; 61:776-786. [DOI: 10.1111/epi.16485] [Citation(s) in RCA: 52] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 03/01/2020] [Accepted: 03/02/2020] [Indexed: 01/22/2023]
Affiliation(s)
- Philippa J. Karoly
- Graeme Clark Institute and St Vincent’s Hospital University of Melbourne Melbourne Victoria Australia
- Department of Biomedical Engineering University of Melbourne Melbourne Victoria Australia
| | - Mark J. Cook
- Graeme Clark Institute and St Vincent’s Hospital University of Melbourne Melbourne Victoria Australia
| | - Matias Maturana
- Graeme Clark Institute and St Vincent’s Hospital University of Melbourne Melbourne Victoria Australia
- Seer Medical Melbourne Victoria Australia
| | - Ewan S. Nurse
- Graeme Clark Institute and St Vincent’s Hospital University of Melbourne Melbourne Victoria Australia
- Seer Medical Melbourne Victoria Australia
| | - Daniel Payne
- Department of Biomedical Engineering University of Melbourne Melbourne Victoria Australia
| | | | - David B. Grayden
- Department of Biomedical Engineering University of Melbourne Melbourne Victoria Australia
| | | | | | | | - Andreas Schulze‐Bonhage
- Faculty of Medicine Epilepsy Center Medical Center University of Freiburg Freiburg Germany
- European Reference Network EpiCare Freiburg Germany
| | - Levin Kuhlmann
- Department of Data Science and AI Faculty of Information Technology Monash University Clayton Victoria Australia
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47
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Stamoulis C, Connoly J, Duffy FH. Data-driven separation and estimation of atrial dynamics in very high-dimensional electrocardiograms from epilepsy patients. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:91-94. [PMID: 31945852 DOI: 10.1109/embc.2019.8856494] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Across biomedical areas, there is a significant unmet need for multimodal biomarkers that can improve prediction of abnormal events such as seizures, heart and asthma attacks or stroke. These markers may be multimodal and may include electrophysiological measures estimated from noninvasive, routinely collected clinical data, such as electroencephalograms (EEG) and electrocardiograms (ECG). In epilepsy, seizure detection and prediction from noninvasive data remains a difficult problem in need of novel approaches and markers. The inherent noise in high-dimensional EEG signals and artifact contamination often severely impacts the sensitivity and specificity of otherwise promising biomarkers. Long-term epilepsy clinical studies typically collect continuous ECG from which additional features may be estimated and combined with EEG measures to improve sensitivity to ictogenesis and seizure specificity. Prior work has focused on ventricular activity and features of the QRS complex, but atrial activity may also be modulated by seizure evolution. Given the high dimension of the ECG collected continuously over several days, an entirely data-driven approach is proposed, based on which ECG signals may be separated into ventricular and atrial contributions and studied separately. The relationship of atrial dynamics to seizure occurrence is assessed in a small number of pediatric epilepsy patients with high-dimensional ECG.
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48
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Gregg NM, Nasseri M, Kremen V, Patterson EE, Sturges BK, Denison TJ, Brinkmann BH, Worrell GA. Circadian and multiday seizure periodicities, and seizure clusters in canine epilepsy. Brain Commun 2020; 2:fcaa008. [PMID: 32161910 PMCID: PMC7052793 DOI: 10.1093/braincomms/fcaa008] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 01/05/2020] [Accepted: 01/08/2020] [Indexed: 01/22/2023] Open
Abstract
Advances in ambulatory intracranial EEG devices have enabled objective analyses of circadian and multiday seizure periodicities, and seizure clusters in humans. This study characterizes circadian and multiday seizure periodicities, and seizure clusters in dogs with naturally occurring focal epilepsy, and considers the implications of an animal model for the study of seizure risk patterns, seizure forecasting and personalized treatment protocols. In this retrospective cohort study, 16 dogs were continuously monitored with ambulatory intracranial EEG devices designed for humans. Detailed medication records were kept for all dogs. Seizure periodicity was evaluated with circular statistics methods. Circular non-uniformity was assessed for circadian, 7-day and approximately monthly periods. The Rayleigh test was used to assess statistical significance, with correction for multiple comparisons. Seizure clusters were evaluated with Fano factor (index of dispersion) measurements, and compared to a Poisson distribution. Relationships between interseizure interval (ISI) and seizure duration were evaluated. Six dogs met the inclusion criteria of having at least 30 seizures and were monitored for an average of 65 weeks. Three dogs had seizures with circadian seizure periodicity, one dog had a 7-day periodicity, and two dogs had approximately monthly periodicity. Four dogs had seizure clusters and significantly elevated Fano factor values. There were subject-specific differences in the dynamics of ISI and seizure durations, both within and between lead and clustered seizure categories. Our findings show that seizure timing in dogs with naturally occurring epilepsy is not random, and that circadian and multiday seizure periodicities, and seizure clusters are common. Circadian, 7-day, and monthly seizure periodicities occur independent of antiseizure medication dosing, and these patterns likely reflect endogenous rhythms of seizure risk.
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Affiliation(s)
- Nicholas M Gregg
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Mona Nasseri
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Vaclav Kremen
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Edward E Patterson
- Department of Veterinary Clinical Sciences, University of Minnesota College of Veterinary Medicine, St Paul, MN 55108, USA
| | - Beverly K Sturges
- Department of Surgical and Radiological Sciences, University of California at Davis School of Veterinary Medicine, Davis, CA 95616, USA
| | - Timothy J Denison
- The Institute of Biomedical Engineering, University of Oxford, Oxford, OX3 7DQ, UK
| | - Benjamin H Brinkmann
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
| | - Gregory A Worrell
- Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN 55905, USA
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49
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Ufongene C, El Atrache R, Loddenkemper T, Meisel C. Electrocardiographic changes associated with epilepsy beyond heart rate and their utilization in future seizure detection and forecasting methods. Clin Neurophysiol 2020; 131:866-879. [PMID: 32066106 DOI: 10.1016/j.clinph.2020.01.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 01/21/2020] [Accepted: 01/24/2020] [Indexed: 12/22/2022]
Abstract
The ability to assess seizure risk may help provide timely warnings and more personalized treatment plans for people with epilepsy (PWE). ECG changes are commonly observed in epilepsy which make ECG a promising candidate to monitor seizure risk. Most ECG research in this domain has focused on heart rate-related changes. However, several studies have identified a range of other peri-ictal ECG parameter changes that may potentially prove useful for seizure detection and forecasting. Here, we offer a systematic review of ECG changes in epilepsy outside of heart rate. We performed the systematic literature review according to PRISMA guidelines using key words related to ECG, SUDEP and epilepsy. We identified and screened 502 abstracts, read 110 full papers, and included 24 papers in the final review. Our results suggest that PWE may be more prone to cardiac conduction abnormalities than healthy controls. During interictal periods, PWE were more likely to have abnormal QTc intervals, ST segment abnormalities, elevated T Waves, early repolarization (ER), increased P Wave dispersion and PR intervals when compared to controls. Apart from these baseline abnormalities, changes during the pre-ictal and ictal states have been reported, with arrhythmias, QTc prolongation and ST segment changes being the most common. A better understanding of these state-dependent changes may afford less-cumbersome and less-stigmatizing epilepsy monitoring tools in the future.
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50
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Dumanis SB, Dixon-Salazar T. Incorporating the regulatory science of patient input for the epilepsy community. Epilepsy Behav 2020; 103:106313. [PMID: 31653604 DOI: 10.1016/j.yebeh.2019.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Accepted: 05/09/2019] [Indexed: 11/26/2022]
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