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Vakilna YS, Li X, Hampson JS, Huang Y, Mosher JC, Dabaghian Y, Luo X, Talavera B, Pati S, Todd M, Hays R, Szabo CA, Zhang GQ, Lhatoo SD. Reliable detection of generalized convulsive seizures using an off-the-shelf digital watch: A multisite phase 2 study. Epilepsia 2024. [PMID: 38738972 DOI: 10.1111/epi.17974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 03/19/2024] [Accepted: 03/25/2024] [Indexed: 05/14/2024]
Abstract
OBJECTIVE The aim of this study was to develop a machine learning algorithm using an off-the-shelf digital watch, the Samsung watch (SM-R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. METHODS This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty-eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave-one-patient-out cross-validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed-and-frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video-electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. RESULTS LOPO CV results showed balanced accuracy of .93 (95% confidence interval [CI] = .8-.98), precision of .68 (95% CI = .46-.85), sensitivity of .87 (95% CI = .62-.96), and FAR of .21/24 h (interquartile range [IQR] = 0-.90). Testing the algorithm on patients without seizure resulted in an FAR of .28/24 h (IQR = 0-.61). During the "fixed-and-frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of .25/24 h (IQR = 0-.89). Feature importance showed that heart rate-based features outperformed accelerometer/gyroscope-based features. SIGNIFICANCE Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom-built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non-EEG-based seizure surveillance and forecasting in the clinical setting.
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Affiliation(s)
- Yash Shashank Vakilna
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Xiaojin Li
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jaison S Hampson
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yan Huang
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - John C Mosher
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Yuri Dabaghian
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Blanca Talavera
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Sandipan Pati
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Masel Todd
- Department of Neurology, University of Texas Medical Branch, Galveston, Texas, USA
| | - Ryan Hays
- Department of Neurology, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Charles Akos Szabo
- Department of Neurology, University of Texas Health Science Center at San Antonio, San Antonio, Texas, USA
| | - Guo-Qiang Zhang
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Samden D Lhatoo
- Department of Neurology, University of Texas Health Science Center at Houston, Houston, Texas, USA
- Texas Institute of Restorative Neurotechnologies, University of Texas Health Science Center at Houston, Houston, Texas, USA
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Bernini A, Dan J, Ryvlin P. Ambulatory seizure detection. Curr Opin Neurol 2024; 37:99-104. [PMID: 38328946 DOI: 10.1097/wco.0000000000001248] [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: 02/09/2024]
Abstract
PURPOSE OF REVIEW To review recent advances in the field of seizure detection in ambulatory patients with epilepsy. RECENT FINDINGS Recent studies have shown that wrist or arm wearable sensors, using 3D-accelerometry, electrodermal activity or photoplethysmography, in isolation or in combination, can reliably detect focal-to-bilateral and generalized tonic-clonic seizures (GTCS), with a sensitivity over 90%, and false alarm rates varying from 0.1 to 1.2 per day. A headband EEG has also demonstrated a high sensitivity for detecting and help monitoring generalized absence seizures. In contrast, no appropriate solution is yet available to detect focal seizures, though some promising findings were reported using ECG-based heart rate variability biomarkers and subcutaneous EEG. SUMMARY Several FDA and/or EU-certified solutions are available to detect GTCS and trigger an alarm with acceptable rates of false alarms. However, data are still missing regarding the impact of such intervention on patients' safety. Noninvasive solutions to reliably detect focal seizures in ambulatory patients, based on either EEG or non-EEG biosignals, remain to be developed. To this end, a number of challenges need to be addressed, including the performance, but also the transparency and interpretability of machine learning algorithms.
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Affiliation(s)
- Adriano Bernini
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), University of Lausanne
| | - Jonathan Dan
- Embedded Systems Laboratory, Swiss Federal Institute of Technology of Lausanne (EPFL), Lausanne, Switzerland
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV), University of Lausanne
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Goldenholz DM, Karoly PJ, Viana PF, Nurse E, Loddenkemper T, Schulze-Bonhage A, Vieluf S, Bruno E, Nasseri M, Richardson MP, Brinkmann BH, Westover MB. Minimum clinical utility standards for wearable seizure detectors: A simulation study. Epilepsia 2024; 65:1017-1028. [PMID: 38366862 PMCID: PMC11018505 DOI: 10.1111/epi.17917] [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: 09/12/2023] [Revised: 01/11/2024] [Accepted: 02/01/2024] [Indexed: 02/18/2024]
Abstract
OBJECTIVE Epilepsy management employs self-reported seizure diaries, despite evidence of seizure underreporting. Wearable and implantable seizure detection devices are now becoming more widely available. There are no clear guidelines about what levels of accuracy are sufficient. This study aimed to simulate clinical use cases and identify the necessary level of accuracy for each. METHODS Using a realistic seizure simulator (CHOCOLATES), a ground truth was produced, which was then sampled to generate signals from simulated seizure detectors of various capabilities. Five use cases were evaluated: (1) randomized clinical trials (RCTs), (2) medication adjustment in clinic, (3) injury prevention, (4) sudden unexpected death in epilepsy (SUDEP) prevention, and (5) treatment of seizure clusters. We considered sensitivity (0%-100%), false alarm rate (FAR; 0-2/day), and device type (external wearable vs. implant) in each scenario. RESULTS The RCT case was efficient for a wide range of wearable parameters, though implantable devices were preferred. Lower accuracy wearables resulted in subtle changes in the distribution of patients enrolled in RCTs, and therefore higher sensitivity and lower FAR values were preferred. In the clinic case, a wide range of sensitivity, FAR, and device type yielded similar results. For injury prevention, SUDEP prevention, and seizure cluster treatment, each scenario required high sensitivity and yet was minimally influenced by FAR. SIGNIFICANCE The choice of use case is paramount in determining acceptable accuracy levels for a wearable seizure detection device. We offer simulation results for determining and verifying utility for specific use case and specific wearable parameters.
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Affiliation(s)
- Daniel M Goldenholz
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Philippa J Karoly
- Department of Neurology, University of Melbourne, Melbourne, Victoria, Australia
| | - Pedro F Viana
- School of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
- Faculty of Medicine, University of Lisbon, Lisbon, Portugal
| | - Ewan Nurse
- Seer Medical, Melbourne, Victoria, Australia
| | - Tobias Loddenkemper
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Andreas Schulze-Bonhage
- Epilepsy Center, University Medical Center Freiburg-University of Freiburg, Freiburg, Germany
| | - Solveig Vieluf
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Elisa Bruno
- School of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - Mona Nasseri
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark P Richardson
- School of Neuroscience, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | | | - M Brandon Westover
- Department of Neurology, Harvard Medical School, Boston, Massachusetts, USA
- Department of Neurology, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
- Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
- McCace Center, Boston, Massachusetts, USA
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Komal K, Cleary F, Wells JSG, Bennett L. A systematic review of the literature reporting on remote monitoring epileptic seizure detection devices. Epilepsy Res 2024; 201:107334. [PMID: 38442551 DOI: 10.1016/j.eplepsyres.2024.107334] [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: 12/13/2023] [Revised: 02/19/2024] [Accepted: 02/26/2024] [Indexed: 03/07/2024]
Abstract
BACKGROUND Early detection and alert notification of an impending seizure for people with epilepsy have the potential to reduce Sudden Unexpected Death in Epilepsy (SUDEP). Current remote monitoring seizure detection devices for people with epilepsy are designed to support real-time monitoring of their vital health parameters linked to seizure alert notification. An understanding of the rapidly growing literature on remote seizure detection devices is essential to address the needs of people with epilepsy and their carers. AIM This review aims to examine the technical characteristics, device performance, user preference, and effectiveness of remote monitoring seizure detection devices. METHODOLOGY A systematic review referenced to PRISMA guidelines was used. RESULTS A total of 1095 papers were identified from the initial search with 30 papers included in the review. Sixteen non-invasive remote monitoring seizure detection devices are currently available. Such seizure detection devices were found to have inbuilt intelligent sensor functionality to monitor electroencephalography, muscle movement, and accelerometer-based motion movement for detecting seizures remotely. Current challenges of these devices for people with epilepsy include skin irritation due to the type of patch electrode used and false alarm notifications, particularly during physical activity. The tight-fitted accelerometer-type devices are reported as uncomfortable from a wearability perspective for long-term monitoring. Also, continuous recording of physiological signals and triggering alert notifications significantly reduce the battery life of the devices. The literature highlights that 3.2 out of 5 people with epilepsy are not using seizure detection devices because of the cost and appearance of the device. CONCLUSION Seizure detection devices can potentially reduce morbidity and mortality for people with epilepsy. Therefore, further collaboration of clinicians, technical experts, and researchers is needed for the future development of these devices. Finally, it is important to always take into consideration the expectations and requirements of people with epilepsy and their carers to facilitate the next generation of remote monitoring seizure detection devices.
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Affiliation(s)
- K Komal
- School of Health Sciences, South East Technological University, Cork Road, Waterford, Ireland; Walton Institute, South East Technological University, Cork Road, Waterford, Ireland.
| | - F Cleary
- Walton Institute, South East Technological University, Cork Road, Waterford, Ireland
| | - J S G Wells
- School of Health Sciences, South East Technological University, Cork Road, Waterford, Ireland
| | - L Bennett
- School of Health Sciences, South East Technological University, Cork Road, Waterford, Ireland
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Donner E, Devinsky O, Friedman D. Wearable Digital Health Technology for Epilepsy. N Engl J Med 2024; 390:736-745. [PMID: 38381676 DOI: 10.1056/nejmra2301913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Affiliation(s)
- Elizabeth Donner
- From the Division of Neurology, Hospital for Sick Children, and the Department of Paediatrics, University of Toronto - both in Toronto (E.D.); and the Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York (O.D., D.F.)
| | - Orrin Devinsky
- From the Division of Neurology, Hospital for Sick Children, and the Department of Paediatrics, University of Toronto - both in Toronto (E.D.); and the Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York (O.D., D.F.)
| | - Daniel Friedman
- From the Division of Neurology, Hospital for Sick Children, and the Department of Paediatrics, University of Toronto - both in Toronto (E.D.); and the Epilepsy Center, Department of Neurology, New York University Grossman School of Medicine, New York (O.D., D.F.)
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Gupta N, Kasula V, Sanmugananthan P, Panico N, Dubin AH, Sykes DAW, D'Amico RS. SmartWear body sensors for neurological and neurosurgical patients: A review of current and future technologies. World Neurosurg X 2024; 21:100247. [PMID: 38033718 PMCID: PMC10682285 DOI: 10.1016/j.wnsx.2023.100247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/24/2023] [Indexed: 12/02/2023] Open
Abstract
Background/objective Recent technological advances have allowed for the development of smart wearable devices (SmartWear) which can be used to monitor various aspects of patient healthcare. These devices provide clinicians with continuous biometric data collection for patients in both inpatient and outpatient settings. Although these devices have been widely used in fields such as cardiology and orthopedics, their use in the field of neurosurgery and neurology remains in its infancy. Methods A comprehensive literature search for the current and future applications of SmartWear devices in the above conditions was conducted, focusing on outpatient monitoring. Findings Through the integration of sensors which measure parameters such as physical activity, hemodynamic variables, and electrical conductivity - these devices have been applied to patient populations such as those at risk for stroke, suffering from epilepsy, with neurodegenerative disease, with spinal cord injury and/or recovering from neurosurgical procedures. Further, these devices are being tested in various clinical trials and there is a demonstrated interest in the development of new technologies. Conclusion This review provides an in-depth evaluation of the use of SmartWear in selected neurological diseases and neurosurgical applications. It is clear that these devices have demonstrated efficacy in a variety of neurological and neurosurgical applications, however challenges such as data privacy and management must be addressed.
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Affiliation(s)
- Nithin Gupta
- Campbell University School of Osteopathic Medicine, Lillington, NC, USA
| | - Varun Kasula
- Campbell University School of Osteopathic Medicine, Lillington, NC, USA
| | | | | | - Aimee H. Dubin
- Campbell University School of Osteopathic Medicine, Lillington, NC, USA
| | - David AW. Sykes
- Department of Neurosurgery, Duke University Medical School, Durham, NC, USA
| | - Randy S. D'Amico
- Lenox Hill Hospital, Department of Neurosurgery, New York, NY, USA
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Sopic D, Teijeiro T, Atienza D, Aminifar A, Ryvlin P. Personalized seizure signature: An interpretable approach to false alarm reduction for long-term epileptic seizure detection. Epilepsia 2023; 64 Suppl 4:S23-S33. [PMID: 35113451 DOI: 10.1111/epi.17176] [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: 10/15/2021] [Revised: 01/13/2022] [Accepted: 01/14/2022] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Long-term automatic detection of focal seizures remains one of the major challenges in epilepsy due to the unacceptably high number of false alarms from state-of-the-art methods. Our aim was to investigate to what extent a new patient-specific approach based on similarly occurring morphological electroencephalographic (EEG) signal patterns could be used to distinguish seizures from nonseizure events, as well as to estimate its maximum performance. METHODS We evaluated our approach on >5500 h of long-term EEG recordings using two public datasets: the PhysioNet.org Children's Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) Scalp EEG database and the EPILEPSIAE European epilepsy database. We visually identified a set of similarly occurring morphological patterns (seizure signature) seen simultaneously over two different EEG channels, and within two randomly selected seizures from each individual. The same seizure signature was then searched for in the entire recording from the same patient using dynamic time warping (DTW) as a similarity metric, with a threshold set to reflect the maximum sensitivity our algorithm could achieve without false alarm. RESULTS At a DTW threshold providing no false alarm during the entire recordings, the mean seizure detection sensitivity across patients was 84%, including 96% for the CHB-MIT database and 74% for the European epilepsy database. A 100% sensitivity was reached in 50% of patients, including 79% from the CHB-MIT database and 27% from the European epilepsy database. The median latency from seizure onset to its detection was 17 ± 10 s, with 84% of seizures being detected within 40 s. SIGNIFICANCE Personalized EEG signature combined with DTW appears to be a promising method to detect ictal events from a limited number of EEG channels with high sensitivity despite low rate of false alarms, high degree of interpretability, and low computational complexity, compatible with its future use in wearable devices.
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Affiliation(s)
- Dionisije Sopic
- Embedded Systems Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Tomas Teijeiro
- Embedded Systems Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - David Atienza
- Embedded Systems Laboratory, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland
| | - Amir Aminifar
- Department of Electrical and Information Technology, Lund University, Lund, Sweden
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Neurology Service, Lausanne University Hospital (Vaud University Hospital Center), University of Lausanne, Lausanne, Switzerland
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Li W, Wang G, Lei X, Sheng D, Yu T, Wang G. Seizure detection based on wearable devices: A review of device, mechanism, and algorithm. Acta Neurol Scand 2022; 146:723-731. [PMID: 36255131 DOI: 10.1111/ane.13716] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Accepted: 09/30/2022] [Indexed: 11/30/2022]
Abstract
With sudden and unpredictable nature, seizures lead to great risk of the secondary damage, status epilepticus, and sudden unexpected death in epilepsy. Thus, it is essential to use a wearable device to detect seizure and inform patients' caregivers for assistant to prevent or relieve adverse consequence. In this review, we gave an account of the current state of the field of seizure detection based on wearable devices from three parts: devices, physiological activities, and algorithms. Firstly, seizure monitoring devices available in the market primarily involve wristband-type devices, patch-type devices, and armband-type devices, which are able to detect motor seizures, focal autonomic seizures, or absence seizures. Secondly, seizure-related physiological activities involve the discharge of brain neurons presented, autonomous nervous activities, and motor. Plenty of studies focus on features from one signal, while it is a lack of evidences about the change of signal coupling along with seizures. Thirdly, the seizure detection algorithms developed from simple threshold method to complicated machine learning and deep learning, aiming at distinguish seizures from normal events. After understanding of some preliminary studies, we will propose our own thought for future development in this field.
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Affiliation(s)
- Wen Li
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Guangming Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Xiyuan Lei
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Duozheng Sheng
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Tao Yu
- Department of Functional Neurosurgery, Beijing Institute of Functional Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Gang Wang
- The Key Laboratory of Biomedical Information Engineering of Ministry of Education, Institute of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
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Esmaeili B, Vieluf S, Dworetzky BA, Reinsberger C. The Potential of Wearable Devices and Mobile Health Applications in the Evaluation and Treatment of Epilepsy. Neurol Clin 2022; 40:729-739. [DOI: 10.1016/j.ncl.2022.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022]
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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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Kok XH, Imtiaz SA, Rodriguez-Villegas E. Assessing the Feasibility of Acoustic Based Seizure Detection. IEEE Trans Biomed Eng 2022; 69:2379-2389. [PMID: 35061585 DOI: 10.1109/tbme.2022.3144634] [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: 11/08/2022]
Abstract
OBJECTIVE Long-term monitoring of epilepsy patients outside of hospital settings is impractical due to the complexity and costs associated with electroencephalogram (EEG) systems. Alternative sensing modalities that can acquire, and automatically interpret signals through easy-to-use wearable devices, are needed to help with at-home management of the disease. In this paper, a novel machine learning algorithm is presented for detecting epileptic seizures using acoustic physiological signals acquired from the neck using a wearable device. METHODS Acoustic signals from an existing database, were processed, to extract their Mel-frequency Cepstral Coefficients (MFCCs) which were used to train RUSBoost classifiers to identify ictal and non-ictal acoustic segments. A postprocessing stage was then applied to the segment classification results to identify seizures episodes. RESULTS Tested on 667 hours of acoustic data acquired from 15 patients with at least one seizure, the algorithm achieved a detection sensitivity of 88.1% (95% CI: 79%-97%) from a total of 36 seizures, out of which 24 had no motor manifestations, with a FPR of 0.83/h, and a median detection latency of -42 s. CONCLUSION The results demonstrated for the first time the ability to identify seizures using acoustic internal body signals acquired on the neck. SIGNIFICANCE The results of this paper validate the feasibility of using internal physiological sounds for seizure detection, which could potentially be of use for the development of novel, wearable, very simple to use, long term monitoring, or seizure detection systems; circumventing the practical limitations of EEG monitoring outside hospital settings, or systems based on sensing modalities that work on convulsive seizures only.
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Chen F, Chen I, Zafar M, Sinha SR, Hu X. Seizures detection using multimodal signals: a scoping review. Physiol Meas 2022; 43. [PMID: 35724654 DOI: 10.1088/1361-6579/ac7a8d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Accepted: 06/20/2022] [Indexed: 11/12/2022]
Abstract
INTRODUCTION Epileptic seizures are common neurological disorders in the world, impacting 50 million people globally. Around 30% of patients with seizures suffer from refractory epilepsy, where seizures are not controlled by medications. The unpredictability of seizures makes it essential to have a continuous seizure monitoring system outside clinical settings for the purpose of minimizing patients' injuries and providing additional pathways for evaluation and treatment follow-up. Autonomic changes related to seizure events have been extensively studied and attempts made to apply them for seizure detection and prediction tasks. This scoping review aims to depict current research activities associated with the implementation of portable, wearable devices for seizure detection or prediction and inform future direction in continuous seizure tracking in ambulatory settings. METHODS Overall methodology framework includes 5 essential stages: research questions identification, relevant studies identification, selection of studies, data charting and summarizing the findings. A systematic searching strategy guided by systematic reviews and meta-analysis (PRISMA) was implemented to identify relevant records on two databases (PubMed, IEEE). RESULTS A total of 30 articles were included in our final analysis. Most of the studies were conducted off-line and employed consumer-graded wearable device. ACM is the dominant modality to be used in seizure detection, and widely deployed algorithms entail Support Vector Machine, Random Forest and threshold-based approach. The sensitivity ranged from 33.2% to 100% for single modality with a false alarm rate (FAR) ranging from 0.096 /day to 14.8 /day. Multimodality has a sensitivity ranging from 51% to 100% with FAR ranging from 0.12/day - 17.7/day. CONCLUSION The overall performance in seizure detection system based on non-cerebral physiological signals is promising, especially for the detection of motor seizures and seizures accompanied with intense ictal autonomic changes.
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Affiliation(s)
- Fangyi Chen
- Biomedical Engineering, Duke University, 305 Teer Engineering Building Box 90271, Durham, North Carolina, 27708, UNITED STATES
| | - Ina Chen
- Biomedical Engineering, Duke University, 305 Teer Engineering Building Box 90271, Durham, North Carolina, 27708, UNITED STATES
| | - Muhammad Zafar
- Department of Paediatrics, Neurology, School of Medicine, Duke University, Duke University Medical Center Greenspace, Durham, North Carolina, 27710, UNITED STATES
| | - Saurabh Ranjan Sinha
- Duke Comprehensive Epilepsy Center, Department of Neurology, School of Medicine, Duke University, 295 Hanes Hse, 330 Trent Drive, Durham, North Carolina, 27710, UNITED STATES
| | - Xiao Hu
- Duke University, 4223 Interprofessional Education Building 307 Trent Drive, Durham, North Carolina, 27710, UNITED STATES
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Naganur V, Sivathamboo S, Chen Z, Kusmakar S, Antonic-Baker A, O'Brien TJ, Kwan P. Automated seizure detection with non-invasive wearable devices: A systematic review and meta-analysis. Epilepsia 2022; 63:1930-1941. [PMID: 35545836 PMCID: PMC9545631 DOI: 10.1111/epi.17297] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 05/08/2022] [Accepted: 05/09/2022] [Indexed: 11/29/2022]
Abstract
Objective This study was undertaken to review the reported performance of noninvasive wearable devices in detecting epileptic seizures and psychogenic nonepileptic seizures (PNES). Methods We conducted a systematic review and meta‐analysis of studies reported up to November 15, 2021. We included studies that used video‐electroencephalographic (EEG) monitoring as the gold standard to determine the sensitivity and false alarm rate (FAR) of noninvasive wearables for automated seizure detection. Results Twenty‐eight studies met the criteria for the systematic review, of which 23 were eligible for meta‐analysis. These studies (1269 patients in total, median recording time = 52.9 h per patient) investigated devices for tonic–clonic seizures using wrist‐worn and/or ankle‐worn devices to measure three‐dimensional accelerometry (15 studies), and/or wearable surface devices to measure electromyography (eight studies). The mean sensitivity for detecting tonic–clonic seizures was .91 (95% confidence interval [CI] = .85–.96, I2 = 83.8%); sensitivity was similar between the wrist‐worn (.93) and surface devices (.90). The overall FAR was 2.1/24 h (95% CI = 1.7–2.6, I2 = 99.7%); FAR was higher in wrist‐worn (2.5/24 h) than in wearable surface devices (.96/24 h). Three of the 23 studies also detected PNES; the mean sensitivity and FAR from these studies were 62.9% and .79/24 h, respectively. Four studies detected both focal and tonic–clonic seizures, and one study detected focal seizures only; the sensitivities ranged from 31.1% to 93.1% in these studies. Significance Reported noninvasive wearable devices had high sensitivity but relatively high FARs in detecting tonic–clonic seizures during limited recording time in a video‐EEG setting. Future studies should focus on reducing FAR, detection of other seizure types and PNES, and longer recording in the community.
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Affiliation(s)
- Vaidehi Naganur
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Medicine, The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, 3000, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Shobi Sivathamboo
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Medicine, The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, 3000, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Zhibin Chen
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Medicine, The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Chronic Disease and Ageing, School of Public Health and Preventive Medicine, Monash University, Melbourne, 3004, Australia
| | - Shitanshu Kusmakar
- Department of Electrical and Electronic Engineering, The University of Melbourne, Victoria, Australia
| | - Ana Antonic-Baker
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia
| | - Terence J O'Brien
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Medicine, The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, 3000, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
| | - Patrick Kwan
- Department of Neuroscience, Central Clinical School, Monash University, Melbourne, 3004, Victoria, Australia.,Department of Medicine, The Royal Melbourne Hospital), The University of Melbourne, 3000, Victoria, Australia.,Department of Neurology, The Royal Melbourne Hospital, Melbourne, 3000, Victoria, Australia.,Department of Neurology, Alfred Health, Melbourne, 3004, Victoria, Australia
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Olmedo-Aguirre JO, Reyes-Campos J, Alor-Hernández G, Machorro-Cano I, Rodríguez-Mazahua L, Sánchez-Cervantes JL. Remote Healthcare for Elderly People Using Wearables: A Review. BIOSENSORS 2022; 12:bios12020073. [PMID: 35200334 PMCID: PMC8869443 DOI: 10.3390/bios12020073] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 01/17/2022] [Accepted: 01/25/2022] [Indexed: 05/21/2023]
Abstract
The growth of health care spending on older adults with chronic diseases faces major concerns that require effective measures to be adopted worldwide. Among the main concerns is whether recent technological advances now offer the possibility of providing remote health care for the aging population. The benefits of suitable prevention and adequate monitoring of chronic diseases by using emerging technological paradigms such as wearable devices and the Internet of Things (IoT) can increase the detection rates of health risks to raise the quality of life for the elderly. Specifically, on the subject of remote health monitoring in older adults, a first approach is required to review devices, sensors, and wearables that serve as tools for obtaining and measuring physiological parameters in order to identify progress, limitations, and areas of opportunity in the development of health monitoring schemes. For these reasons, a review of articles on wearable devices was presented in the first instance to identify whether the selected articles addressed the needs of aged adults. Subsequently, the direct review of commercial and prototype wearable devices with the capability to read physiological parameters was presented to identify whether they are optimal or usable for health monitoring in older adults.
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Affiliation(s)
- José Oscar Olmedo-Aguirre
- Department of Electrical Engineering, CINVESTAV-IPN, Av. Instituto Politécnico Nacional 2 508, Col. San Pedro Zacatenco, Delegación Gustavo A. Madero, Mexico City C.P. 07360, Mexico;
| | - Josimar Reyes-Campos
- Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, Mexico; (J.R.-C.); (L.R.-M.)
| | - Giner Alor-Hernández
- Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, Mexico; (J.R.-C.); (L.R.-M.)
- Correspondence: ; Tel./Fax: +52-272-725-7056
| | - Isaac Machorro-Cano
- Universidad del Papaloapan, Circuito Central #200, Col. Parque Industrial, Tuxtepec C.P. 68301, Oaxaca, Mexico;
| | - Lisbeth Rodríguez-Mazahua
- Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, Mexico; (J.R.-C.); (L.R.-M.)
| | - José Luis Sánchez-Cervantes
- CONACYT-Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, Mexico;
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15
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Bongers J, Gutierrez-Quintana R, Stalin CE. Owner's Perception of Seizure Detection Devices in Idiopathic Epileptic Dogs. Front Vet Sci 2021; 8:792647. [PMID: 34966815 PMCID: PMC8711717 DOI: 10.3389/fvets.2021.792647] [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: 10/10/2021] [Accepted: 11/15/2021] [Indexed: 11/13/2022] Open
Abstract
Accurate knowledge of seizure frequency is key to optimising treatment. New methods for detecting epileptic seizures are currently investigated in humans, which rely on changes in biomarkers, also called seizure detection devices. Critical to device development, is understanding user needs and requirements. No information on this subject has been published in veterinary medicine. Many dog health collars are currently on the market, but none has proved to be a promising seizure detector. An online survey was created and consisted of 27 open, closed, and scaled questions divided over two parts: part one focused on general questions related to signalment and seizure semiology, the second part focused specifically on the use of seizure detection devices. Two hundred and thirty-one participants caring for a dog with idiopathic epilepsy, were included in the study. Open questions were coded using descriptive coding by two of the authors independently. Data was analysed using descriptive statistics and binary logistic regression. Our results showed that the unpredictability of seizures plays a major part in the management of canine epilepsy and dog owners have a strong desire to know when a seizure occurs. Nearly all dog owners made changes in their daily life, mainly focusing on intensifying supervision. Owners believed seizure detection devices would improve their dog's seizure management, including a better accuracy of seizure frequency and the ability to administer emergency drugs more readily. Owners that were already keeping track of their dog's seizures were 4.2 times more likely to show confidence in using seizure detection devices to manage their pet's seizures, highlighting the need for better monitoring systems. Our results show that there is a receptive market for wearable technology as a new management strategy in canine epilepsy and this topic should be further explored.
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Affiliation(s)
- Jos Bongers
- Neurology and Neurosurgery Service, The School of Veterinary Medicine, College of Medicine, Veterinary Medicine and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Rodrigo Gutierrez-Quintana
- Neurology and Neurosurgery Service, The School of Veterinary Medicine, College of Medicine, Veterinary Medicine and Life Sciences, University of Glasgow, Glasgow, United Kingdom
| | - Catherine Elizabeth Stalin
- Neurology and Neurosurgery Service, The School of Veterinary Medicine, College of Medicine, Veterinary Medicine and Life Sciences, University of Glasgow, Glasgow, United Kingdom
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16
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Böttcher S, Bruno E, Manyakov NV, Epitashvili N, Claes K, Glasstetter M, Thorpe S, Lees S, Dümpelmann M, Van Laerhoven K, Richardson MP, Schulze-Bonhage A. Detecting Tonic-Clonic Seizures in Multimodal Biosignal Data From Wearables: Methodology Design and Validation. JMIR Mhealth Uhealth 2021; 9:e27674. [PMID: 34806993 PMCID: PMC8663471 DOI: 10.2196/27674] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 07/23/2021] [Accepted: 09/03/2021] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Video electroencephalography recordings, routinely used in epilepsy monitoring units, are the gold standard for monitoring epileptic seizures. However, monitoring is also needed in the day-to-day lives of people with epilepsy, where video electroencephalography is not feasible. Wearables could fill this gap by providing patients with an accurate log of their seizures. OBJECTIVE Although there are already systems available that provide promising results for the detection of tonic-clonic seizures (TCSs), research in this area is often limited to detection from 1 biosignal modality or only during the night when the patient is in bed. The aim of this study is to provide evidence that supervised machine learning can detect TCSs from multimodal data in a new data set during daytime and nighttime. METHODS An extensive data set of biosignals from a multimodal watch worn by people with epilepsy was recorded during their stay in the epilepsy monitoring unit at 2 European clinical sites. From a larger data set of 243 enrolled participants, those who had data recorded during TCSs were selected, amounting to 10 participants with 21 TCSs. Accelerometry and electrodermal activity recorded by the wearable device were used for analysis, and seizure manifestation was annotated in detail by clinical experts. Ten accelerometry and 3 electrodermal activity features were calculated for sliding windows of variable size across the data. A gradient tree boosting algorithm was used for seizure detection, and the optimal parameter combination was determined in a leave-one-participant-out cross-validation on a training set of 10 seizures from 8 participants. The model was then evaluated on an out-of-sample test set of 11 seizures from the remaining 2 participants. To assess specificity, we additionally analyzed data from up to 29 participants without TCSs during the model evaluation. RESULTS In the leave-one-participant-out cross-validation, the model optimized for sensitivity could detect all 10 seizures with a false alarm rate of 0.46 per day in 17.3 days of data. In a test set of 11 out-of-sample TCSs, amounting to 8.3 days of data, the model could detect 10 seizures and produced no false positives. Increasing the test set to include data from 28 more participants without additional TCSs resulted in a false alarm rate of 0.19 per day in 78 days of wearable data. CONCLUSIONS We show that a gradient tree boosting machine can robustly detect TCSs from multimodal wearable data in an original data set and that even with very limited training data, supervised machine learning can achieve a high sensitivity and low false-positive rate. This methodology may offer a promising way to approach wearable-based nonconvulsive seizure detection.
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Affiliation(s)
- Sebastian Böttcher
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany.,Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Elisa Bruno
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Nikolay V Manyakov
- Data Science Analytics & Insights, Janssen Research & Development, Beerse, Belgium
| | - Nino Epitashvili
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | | | - Martin Glasstetter
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | - Sarah Thorpe
- The RADAR-CNS Patient Advisory Board, King's College London, London, United Kingdom
| | - Simon Lees
- The RADAR-CNS Patient Advisory Board, King's College London, London, United Kingdom
| | - Matthias Dümpelmann
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
| | - Kristof Van Laerhoven
- Ubiquitous Computing, Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
| | - Mark P Richardson
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom.,National Institute of Health Research Biomedical Research Centre, South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center - University of Freiburg, Freiburg im Breisgau, Germany
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- see Acknowledgements, London, United Kingdom
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17
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Hubbard I, Beniczky S, Ryvlin P. The Challenging Path to Developing a Mobile Health Device for Epilepsy: The Current Landscape and Where We Go From Here. Front Neurol 2021; 12:740743. [PMID: 34659099 PMCID: PMC8517120 DOI: 10.3389/fneur.2021.740743] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Accepted: 09/03/2021] [Indexed: 11/13/2022] Open
Abstract
Seizure detection, and more recently seizure forecasting, represent important avenues of clinical development in epilepsy, promoted by progress in wearable devices and mobile health (mHealth), which might help optimizing seizure control and prevention of seizure-related mortality and morbidity in persons with epilepsy. Yet, very long-term continuous monitoring of seizure-sensitive biosignals in the ambulatory setting presents a number of challenges. We herein provide an overview of these challenges and current technological landscape of mHealth devices for seizure detection. Specifically, we display, which types of sensor modalities and analytical methods are available, and give insight into current clinical practice guidelines, main outcomes of clinical validation studies, and discuss how to evaluate device performance at point-of-care facilities. We then address pitfalls which may arise in patient compliance and the need to design solutions adapted to user experience.
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Affiliation(s)
- Ilona Hubbard
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - Sandor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Center, Dianalund, Denmark.,Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
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18
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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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19
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Glasstetter M, Böttcher S, Zabler N, Epitashvili N, Dümpelmann M, Richardson MP, Schulze-Bonhage A. Identification of Ictal Tachycardia in Focal Motor- and Non-Motor Seizures by Means of a Wearable PPG Sensor. SENSORS (BASEL, SWITZERLAND) 2021; 21:6017. [PMID: 34577222 PMCID: PMC8470979 DOI: 10.3390/s21186017] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 08/31/2021] [Accepted: 09/01/2021] [Indexed: 11/16/2022]
Abstract
Photoplethysmography (PPG) as an additional biosignal for a seizure detector has been underutilized so far, which is possibly due to its susceptibility to motion artifacts. We investigated 62 focal seizures from 28 patients with electrocardiography-based evidence of ictal tachycardia (IT). Seizures were divided into subgroups: those without epileptic movements and those with epileptic movements not affecting and affecting the extremities. PPG-based heart rate (HR) derived from a wrist-worn device was calculated for sections with high signal quality, which were identified using spectral entropy. Overall, IT based on PPG was identified in 37 of 62 (60%) seizures (9/19, 7/8, and 21/35 in the three groups, respectively) and could be found prior to the onset of epileptic movements affecting the extremities in 14/21 seizures. In 30/37 seizures, PPG-based IT was in good temporal agreement (<10 s) with ECG-based IT, with an average delay of 5.0 s relative to EEG onset. In summary, we observed that the identification of IT by means of a wearable PPG sensor is possible not only for non-motor seizures but also in motor seizures, which is due to the early manifestation of IT in a relevant subset of focal seizures. However, both spontaneous and epileptic movements can impair PPG-based seizure detection.
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Affiliation(s)
- Martin Glasstetter
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Sebastian Böttcher
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Nicolas Zabler
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Nino Epitashvili
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Matthias Dümpelmann
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
| | - Mark P. Richardson
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience King’s College London, London SE5 9RT, UK;
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center—University of Freiburg, 79106 Freiburg im Breisgau, Germany; (S.B.); (N.Z.); (N.E.); (M.D.); (A.S.-B.)
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20
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Onorati F, Regalia G, Caborni C, LaFrance WC, Blum AS, Bidwell J, De Liso P, El Atrache R, Loddenkemper T, Mohammadpour-Touserkani F, Sarkis RA, Friedman D, Jeschke J, Picard R. Prospective Study of a Multimodal Convulsive Seizure Detection Wearable System on Pediatric and Adult Patients in the Epilepsy Monitoring Unit. Front Neurol 2021; 12:724904. [PMID: 34489858 PMCID: PMC8418082 DOI: 10.3389/fneur.2021.724904] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 07/27/2021] [Indexed: 12/03/2022] Open
Abstract
Background: Using machine learning to combine wrist accelerometer (ACM) and electrodermal activity (EDA) has been shown effective to detect primarily and secondarily generalized tonic-clonic seizures, here termed as convulsive seizures (CS). A prospective study was conducted for the FDA clearance of an ACM and EDA-based CS-detection device based on a predefined machine learning algorithm. Here we present its performance on pediatric and adult patients in epilepsy monitoring units (EMUs). Methods: Patients diagnosed with epilepsy participated in a prospective multi-center clinical study. Three board-certified neurologists independently labeled CS from video-EEG. The Detection Algorithm was evaluated in terms of Sensitivity and false alarm rate per 24 h-worn (FAR) on all the data and on only periods of rest. Performance were analyzed also applying the Detection Algorithm offline, with a less sensitive but more specific parameters configuration (“Active mode”). Results: Data from 152 patients (429 days) were used for performance evaluation (85 pediatric aged 6–20 years, and 67 adult aged 21–63 years). Thirty-six patients (18 pediatric) experienced a total of 66 CS (35 pediatric). The Sensitivity (corrected for clustered data) was 0.92, with a 95% confidence interval (CI) of [0.85-1.00] for the pediatric population, not significantly different (p > 0.05) from the adult population's Sensitivity (0.94, CI: [0.89–1.00]). The FAR on the pediatric population was 1.26 (CI: [0.87–1.73]), higher (p < 0.001) than in the adult population (0.57, CI: [0.36–0.81]). Using the Active mode, the FAR decreased by 68% while reducing Sensitivity to 0.95 across the population. During rest periods, the FAR's were 0 for all patients, lower than during activity periods (p < 0.001). Conclusions: Performance complies with FDA's requirements of a lower bound of CI for Sensitivity higher than 0.7 and of a FAR lower than 2, for both age groups. The pediatric FAR was higher than the adult FAR, likely due to higher pediatric activity. The high Sensitivity and precision (having no false alarms) during sleep might help mitigate SUDEP risk by summoning caregiver intervention. The Active mode may be advantageous for some patients, reducing the impact of the FAR on daily life. Future work will examine the performance and usability outside of EMUs.
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Affiliation(s)
| | | | | | - W Curt LaFrance
- Division of Neuropsychiatry and Behavioral Neurology, Rhode Island Hospital, Brown University, Providence, RI, United States
| | - Andrew S Blum
- Department of Neurology, Rhode Island Hospital, Brown University, Providence, RI, United States
| | | | - Paola De Liso
- Department of Neuroscience, Bambino Gesù Children's Hospital, Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Rima El Atrache
- Department of Neurology, Boston Children's Hospital, Boston, MA, United States
| | - Tobias Loddenkemper
- Department of Neurology, Boston Children's Hospital, Boston, MA, United States
| | | | - Rani A Sarkis
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, United States
| | - Daniel Friedman
- Department of Neurology, New York University Langone Medical Center, New York, NY, United States
| | - Jay Jeschke
- Department of Neurology, New York University Langone Medical Center, New York, NY, United States
| | - Rosalind Picard
- Empatica, Inc., Boston, MA, United States.,MIT Media Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
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21
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Husain AM, Towne AR, Chen DK, Whitmire LE, Voyles SR, Cardenas DP. Differentiation of Epileptic and Psychogenic Nonepileptic Seizures Using Single-Channel Surface Electromyography. J Clin Neurophysiol 2021; 38:432-438. [PMID: 32501944 DOI: 10.1097/wnp.0000000000000703] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
PURPOSE Epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) are difficult to differentiate when based on a patient's self-reported symptoms. This study proposes review of objective data captured by a surface electromyography (sEMG) wearable device for classification of events as ES or PNES. This may help clinicians accurately identify ES and PNES. METHODS Seventy-one subjects were prospectively enrolled across epilepsy monitoring units at VA Epilepsy Centers of Excellence. Subjects were concomitantly monitored using video EEG and a wearable sEMG epilepsy monitor, the Sensing Portable sEmg Analysis Characterization (SPEAC) System. Three epileptologists independently classified ES and PNES that contained upper extremity motor activity based on video EEG. The sEMG data from those events were automatically processed to provide a seizure score for event classification. After brief training (60 minutes), the sEMG data were reviewed by a separate group of four epileptologists to independently classify events as ES or PNES. RESULTS According to video EEG review, 17 subjects experienced 34 events (15 ES and 19 PNES with upper extremity motor activity). The automated process correctly classified 87% of ES (positive predictive value = 88%, negative predictive value = 76%) and 79% of PNES, and the expert reviewers correctly classified 77% of ES (positive predictive value = 94%, negative predictive value = 84%) and 96% of PNES. The automated process and the expert reviewers correctly classified 100% of tonic-clonic seizures as ES, and 71 and 50%, respectively, of non-tonic-clonic ES. CONCLUSIONS Automated and expert review, particularly in combination, of sEMG captured by a wearable seizure monitor (SPEAC System) may be able to differentiate ES (especially tonic-clonic) and PNES with upper extremity motor activity.
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Affiliation(s)
- Aatif M Husain
- Department of Neurology, Duke University Medical Center, Durham, North Carolina, U.S.A
- Neurosciences Medicine, Duke Clinical Research Institute, Durham, North Carolina, U.S.A
- Neurodiagnostic Center, Veterans Affairs Medical Center Neuroscience Medicine, Durham, North Carolina, U.S.A
| | - Alan R Towne
- Virginia Commonwealth University, Richmond, Virginia, U.S.A
- Department of Veterans Affairs, Northeast Epilepsy Center of Excellence, Hunter Holmes McGuire Veterans Affairs Medical Center, Richmond, Virginia, U.S.A
| | - David K Chen
- Department of Neurology, Baylor College of Medicine, Southwest Epilepsy Center of Excellence, Michael E. DeBakey VA Medical Center, Houston, Texas, U.S.A.; and
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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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Frankel MA, Lehmkuhle MJ, Watson M, Fetrow K, Frey L, Drees C, Spitz MC. Electrographic seizure monitoring with a novel, wireless, single-channel EEG sensor. Clin Neurophysiol Pract 2021; 6:172-178. [PMID: 34189361 PMCID: PMC8220094 DOI: 10.1016/j.cnp.2021.04.003] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 03/21/2021] [Accepted: 04/26/2021] [Indexed: 11/18/2022] Open
Abstract
Objective Recording seizures using personal seizure diaries can be challenging during everyday life and many seizures are missed or mis-reported. People living with epilepsy could benefit by having a more accurate and objective wearable EEG system for counting seizures that can be used outside of the hospital. The objective of this study was to (1) determine which seizure types can be electrographically recorded from the scalp below the hairline, (2) determine epileptologists' ability to identify electrographic seizures from single-channels extracted from full-montage wired-EEG, and (3) determine epileptologists' ability to identify electrographic seizures from Epilog, a wireless single-channel EEG sensor. Methods Epilog sensors were worn concurrently during epilepsy monitoring unit (EMU) monitoring. During standard-of-care review, epileptologists were asked if the electrographic portion of the seizure was visible on single channels of wired electrodes at locations proximal to Epilog sensors, and if focal-onset, which electrode was closest to the focus. From these locations, single channels of EEG extracted from wired full-montage EEG and the proximal Epilog sensor were presented to 3 blinded epileptologists along with markers for when known seizures occurred (taken from the standard-of-care review). Control segments at inter-ictal times were included as control. The epileptologists were asked whether a seizure event was visible in the single channel EEG record at or near the marker. Results A total of 75 seizures were recorded from 22 of 40 adults that wore Epilog during their visit to the EMU. Epileptologists were able to visualize known seizure activity on at least one of the wired electrodes proximal to Epilog sensors for all seizure events. Epileptologists accurately identified seizures in 71% of Epilog recordings and 84% of single-channel wired recordings and were 92% accurate identifying seizures with Epilog when those seizures ended in a clinical convulsion compared to those that did not (>55%). Conclusions Epileptologists are able to visualize seizure activity on single-channels of EEG at locations where Epilog sensors are easily placed on the scalp below hairline. Manual review of seizure annotations can be done quickly and accurately (>70% TP and >98% PPV) on single-channel EEG data. Reviewing single-channel EEG is more accurate than what has been reported in the literature on self-reporting seizures in seizure diaries, the current standard of care for seizure counting outside of the EMU. Significance Wearable EEG will be important for seizure monitoring outside of the hospital. Epileptologists can accurately identify seizures in single-channel EEG, better than patient self-reporting in diaries based on the literature. Automated or semi-automated seizure detection on single channels of EEG could be used in the future to objectively count seizures to complement the standard of care outside of the EMU without the overt burden upon epileptologist review.
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Affiliation(s)
| | - Mark J. Lehmkuhle
- Department of Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, USA
- Corresponding author at: Epitel, Inc., 124 South 400 East, Suite 450, Salt Lake City, UT 84111, USA.
| | - Meagan Watson
- Department of Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, USA
| | - Kirsten Fetrow
- Department of Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, USA
| | - Lauren Frey
- Department of Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, USA
| | - Cornelia Drees
- Department of Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, USA
| | - Mark C. Spitz
- Department of Neurology, University of Colorado Anschutz Medical Center, Aurora, CO, USA
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24
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Affiliation(s)
- Mark Manford
- Neurology, Cambridge University, Cambridge CB2 1TN, UK
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25
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Bacher D, Amini A, Friedman D, Doyle W, Pacia S, Kuzniecky R. Validation of an EEG seizure detection paradigm optimized for clinical use in a chronically implanted subcutaneous device. J Neurosci Methods 2021; 358:109220. [PMID: 33971201 DOI: 10.1016/j.jneumeth.2021.109220] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 04/27/2021] [Accepted: 05/03/2021] [Indexed: 12/25/2022]
Abstract
BACKGROUND Many electroencephalography (EEG) based seizure detection paradigms have been developed and validated over the last two decades. The majority of clinical approaches use scalp or intracranial EEG electrodes. Scalp EEG is limited by patient discomfort and short duration of useful EEG signals. Intracranial EEG involves an invasive surgical procedure associated with significant risk making it unsuitable for widespread use as a practical clinical biometric. A less invasive EEG monitoring approach, that is between invasive intracranial procedures and noninvasive methods, would fill the need of a safe, accurate, chronic (ultra-long term) and objective seizure detection method. We present validation of a continuous EEG seizure detection paradigm using human single-channel EEG recordings from subcutaneously placed electrodes that could be used to fulfill this need. METHODS Ten-minute long sleep, awake and ictal EEG epochs obtained from 21 human subjects with subscalp electrodes and validated against simultaneous iEEG recordings were analyzed by three experienced clinical neurophysiologists. The 201subscalp EEG time series epochs where classified as diagnostic for awake, asleep, or seizure by the clinicians who were blinded to all other information. Seventy of the epochs were classified in this way as representing seizure activity. A subject specific seizure detection algorithm was trained and then evaluated offline for each patient in the data set using the expert consensus classification as the gold standard. RESULTS The average seizure detection performance of the algorithm across 21 subjects exceeded 90 % accuracy: 97 % sensitivity, 91 % specificity, and 93 % accuracy. For 19 of 21 patient datasets the algorithm achieved 100 % sensitivity. For 15 of 21 patients, the algorithm achieved 100 % specificity. For 13 of 21 patients the algorithm achieved 100 % accuracy. COMPARISON No comparable published methods are available for subgaleal EEG seizure detection. CONCLUSIONS These findings suggest that a simple seizure detection algorithm using subcutaneous EEG signals could provide sufficient accuracy and clinical utility for use in a low power, long-term subcutaneous brain monitoring device. Such a device would fill a need for a large number of people with epilepsy who currently have no means for accurately quantifying their seizures thereby providing important information to healthcare providers not currently available.
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Affiliation(s)
| | - Andrew Amini
- Department of Neurology and Neurosurgery, NYU Langone School of Medicine, New York, United States
| | - Daniel Friedman
- Department of Neurology and Neurosurgery, NYU Langone School of Medicine, New York, United States
| | - Werner Doyle
- Department of Neurology and Neurosurgery, NYU Langone School of Medicine, New York, United States
| | - Steven Pacia
- Department of Neurology, Zucker Hofstra School of Medicine, New York, United States
| | - Ruben Kuzniecky
- Department of Neurology, Zucker Hofstra School of Medicine, New York, United States.
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26
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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: 112] [Impact Index Per Article: 37.3] [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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27
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Nasseri M, Pal Attia T, Joseph B, Gregg NM, Nurse ES, Viana PF, Schulze-Bonhage A, Dümpelmann M, Worrell G, Freestone DR, Richardson MP, Brinkmann BH. Non-invasive wearable seizure detection using long-short-term memory networks with transfer learning. J Neural Eng 2021; 18. [PMID: 33730713 DOI: 10.1088/1741-2552/abef8a] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 03/17/2021] [Indexed: 11/12/2022]
Abstract
Objective. The detection of seizures using wearable devices would improve epilepsy management, but reliable detection of seizures in an ambulatory environment remains challenging, and current studies lack concurrent validation of seizures using electroencephalography (EEG) data.Approach. An adaptively trained long-short-term memory deep neural network was developed and trained using a modest number of seizure data sets from wrist-worn devices. Transfer learning was used to adapt a classifier that was initially trained on intracranial electroencephalography (iEEG) signals to facilitate classification of non-EEG physiological datasets comprising accelerometry, blood volume pulse, skin electrodermal activity, heart rate, and temperature signals. The algorithm's performance was assessed with and without pre-training on iEEG signals and transfer learning. To assess the performance of the seizure detection classifier using long-term ambulatory data, wearable devices were used for multiple months with an implanted neurostimulator capable of recording iEEG signals, which provided independent electrographic seizure detections that were reviewed by a board-certified epileptologist.Main results. For 19 motor seizures from 10 in-hospital patients, the algorithm yielded a mean area under curve (AUC), a sensitivity, and an false alarm rate per day (FAR/day) of 0.98, 0.93, and 2.3, respectively. Additionally, for eight seizures with probable motor semiology from two ambulatory patients, the classifier achieved a mean AUC of 0.97 and an FAR of 2.45 events/day at a sensitivity of 0.9. For all seizure types in the ambulatory setting, the classifier had a mean AUC of 0.82 with a sensitivity of 0.47 and an FAR of 7.2 events/day.Significance. The performance of the algorithm was evaluated using motor and non-motor seizures during in-hospital and ambulatory use. The classifier was able to detect multiple types of motor and non-motor seizures, but performed significantly better on motor seizures.
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Affiliation(s)
- Mona Nasseri
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Alfred 9-441C, 200 First Street SW, Rochester, MN 55905, United States of America.,School of Engineering, University of North Florida, Jacksonville, FL, United States of America
| | - Tal Pal Attia
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Alfred 9-441C, 200 First Street SW, Rochester, MN 55905, United States of America
| | - Boney Joseph
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Alfred 9-441C, 200 First Street SW, Rochester, MN 55905, United States of America
| | - Nicholas M Gregg
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Alfred 9-441C, 200 First Street SW, Rochester, MN 55905, United States of America
| | - Ewan S Nurse
- Seer Medical Pty Ltd, Melbourne, VIC, Australia.,Department of Medicine, St. Vincent's Hospital Melbourne, University of Melbourne, Melbourne, VIC, Australia
| | - 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
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Matthias Dümpelmann
- Department of Neurosurgery, Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Gregory Worrell
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Alfred 9-441C, 200 First Street SW, Rochester, MN 55905, United States of America
| | - Dean R Freestone
- Seer Medical Pty Ltd, Melbourne, VIC, Australia.,Department of Medicine, St. Vincent's Hospital Melbourne, University of Melbourne, Melbourne, VIC, Australia
| | - Mark P Richardson
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Benjamin H Brinkmann
- Bioelectronics Neurology and Engineering Laboratory, Department of Neurology, Mayo Clinic, Alfred 9-441C, 200 First Street SW, Rochester, MN 55905, United States of America
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28
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Beniczky S, Wiebe S, Jeppesen J, Tatum WO, Brazdil M, Wang Y, Herman ST, Ryvlin P. Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology. Epilepsia 2021; 62:632-646. [PMID: 33666944 DOI: 10.1111/epi.16818] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 12/23/2020] [Accepted: 12/23/2020] [Indexed: 12/15/2022]
Abstract
The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.
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Affiliation(s)
- Sándor Beniczky
- Department of Clinical Neurophysiology, Danish Epilepsy Centre and Aarhus University Hospital, Dianalund, Denmark.,Department of Clinical Medicine, Aarhus University, Aarhus C, Denmark
| | - Samuel Wiebe
- Department of Clinical Neurosciences and Clinical Research Unit, Cumming School of Medicine, University of Calgary, Calgary, Canada
| | - Jesper Jeppesen
- Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus C, Denmark
| | - William O Tatum
- Department of Neurology, Mayo Clinic, Jacksonville, Florida, USA
| | - Milan Brazdil
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic.,Behavioral and Social Neuroscience Research Group, Central European Institute of Technology, Masaryk University, Brno, Czech Republic
| | - Yuping Wang
- Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Susan T Herman
- Department of Neurology, Barrow Neurological Institute, Phoenix, AZ, USA
| | - Philippe Ryvlin
- Department of Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Université de Lausanne, Lausanne, Switzerland
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Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology. Clin Neurophysiol 2021; 132:1173-1184. [PMID: 33678577 DOI: 10.1016/j.clinph.2020.12.009] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy, on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found moderate level of evidence for seizure types without GTCs or FBTCs. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.
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30
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Surges R. Wearables bei Epilepsien. KLIN NEUROPHYSIOL 2021. [DOI: 10.1055/a-1353-9099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
ZusammenfassungEpileptische Anfälle führen zu verschiedensten körperlichen Symptomen, die je nach Art und Ausprägung mit geeigneten Geräten gemessen werden und als Surrogatmarker epileptischer Anfälle dienen können. Dominierende motorische Symptome können mit Beschleunigungssensoren oder elektromyografisch erfasst werden. Bei fokalen Anfällen mit fehlender oder geringer motorischer Beteiligung können autonome Phänomene wie Änderungen der Herzrate, Atmung und des elektrischen Hautwiderstandes per Elektrokardiografie, Photopletysmografie und Hautsensoren gemessen werden. Die in den heutigen Wearables integrierten Sensoren können diese Körpersignale messen und zur automatisierten Anfallserkennung nutzbar machen. In dieser Übersichtsarbeit werden verschiedene Sensortechnologien, Wearables und deren Anwendung zur automatisierten Erkennung epileptischer Anfälle vorgestellt, Gütekriterien zur Einschätzung mobiler Gesundheitstechnologien diskutiert und klinisch geprüfte Systeme zusammengefasst.
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31
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Baumgartner C, Whitmire LE, Voyles SR, Cardenas DP. Using sEMG to identify seizure semiology of motor seizures. Seizure 2021; 86:52-59. [PMID: 33550134 DOI: 10.1016/j.seizure.2020.11.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/20/2020] [Accepted: 11/19/2020] [Indexed: 11/18/2022] Open
Abstract
PURPOSE Accurate characterization and quantification of seizure types are critical for optimal pharmacotherapy in epilepsy patients. Technological advances have made it possible to continuously monitor physiological signals within or outside the hospital setting. This study tested the utility of single-channel surface-electromyography (sEMG) for characterization of motor epileptic seizure semiology. METHODS Seventy-one subjects were prospectively enrolled where vEEG and sEMG were simultaneously recorded. Three epileptologists independently identified and classified seizure events with upper-extremity (UE) motor activity by reviewing vEEG, serving as a clinical standard. Surface EMG recorded during the events identified by the clinical standard were evaluated using automated classification methods and expert review by a second group of three independent epileptologists (blinded to the vEEG data). Surface EMG classification categories included: tonic-clonic (TC), tonic only, clonic only, or other motor seizures. Both automated and expert review of sEMG was compared to the clinical standard. RESULTS Twenty subjects experienced 47 motor seizures. Automated sEMG event classification methods accurately classified 72 % (95 % CI [0.57, 0.84]) of events (15/18 TC seizures, 5/9 tonic seizures, 1/3 clonic seizures, and 13/17 other seizures). Three independent reviewers' majority-rule analysis of sEMG correctly classified 81 % (95 % CI [0.67, 0.91]) of events (16/18 TC seizures, 8/9 tonic seizures, 1/3 clonic seizures, and 13/17 other manifestations). CONCLUSIONS Continuous monitoring of sEMG data provides an objective measure to evaluate motor seizure activity. Using sEMG from a wearable monitor recorded from the biceps, automated and expert review may be used to characterize the semiology of events with UE motor activity, particularly TC and tonic seizures.
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Affiliation(s)
- Christoph Baumgartner
- Department of Neurology, General Hospital Hietzing With Neurological Center Rosenhügel, Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Medical Faculty, Sigmund Freud University, Vienna, Austria
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32
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Jacobsen M, Dembek TA, Kobbe G, Gaidzik PW, Heinemann L. Noninvasive Continuous Monitoring of Vital Signs With Wearables: Fit for Medical Use? J Diabetes Sci Technol 2021; 15:34-43. [PMID: 32063034 PMCID: PMC7783016 DOI: 10.1177/1932296820904947] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Wearables (= wearable computer) enable continuous and noninvasive monitoring of a range of vital signs. Mobile and cost-effective devices, combined with powerful data analysis tools, open new dimensions in assessing body functions ("digital biomarkers"). METHODS To answer the question whether wearables are ready for use in the medical context, a PubMed literature search and analysis for their clinical-scientific use using publications from the years 2008 to 2018 was performed. RESULTS A total of 79 out of 314 search hits were publications on clinical trials with wearables, of which 16 were randomized controlled trials. Motion sensors were most frequently used to measure defined movements, movement disorders, or general physical activity. Approximately 20% of the studies used sensors to detect cardiovascular parameters. As for the sensor location, the wrist was chosen in most studies (22.8%). CONCLUSION Wearables can be used in a precisely defined medical context, when taking into account complex influencing factors.
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Affiliation(s)
- Malte Jacobsen
- University Witten/Herdecke, Germany
- Malte Jacobsen, MD, University Witten/Herdecke, Alfred-Herrhausen-Straße 50, 58455 Witten, Germany.
| | - Till A. Dembek
- Department of Neurology, University Hospital of Cologne, Germany
| | - Guido Kobbe
- Clinic for Hematology, Oncology and Clinical Immunology, University Hospital Düsseldorf, Germany
| | - Peter W. Gaidzik
- Institute for Health Care Law, University Witten/Herdecke, Germany
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Blek N, Zwoliński P. mHealth tools in the management of epilepsy. JOURNAL OF EPILEPTOLOGY 2020. [DOI: 10.21307/jepil-2020-007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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Verdru J, Van Paesschen W. Wearable seizure detection devices in refractory epilepsy. Acta Neurol Belg 2020; 120:1271-1281. [PMID: 32632710 DOI: 10.1007/s13760-020-01417-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Accepted: 06/29/2020] [Indexed: 12/01/2022]
Abstract
Epilepsy affects 50 million patients and their caregivers worldwide. Devices that facilitate the detection of seizures can have a large influence on a patient's quality of life, therapeutic decisions and the conduct of clinical trials with anti-epileptic drugs. This article provides an up-to-date overview and comparison between wearable seizure detection devices (WSDDs), taking into account the newly proposed standards for testing and clinical validation of devices. 16 devices were included in our comparison. The F1-score, combining the device's accurate recall and precision, was calculated for each of these devices and used to evaluate their performance. The devices were separated by development phase and ranked by F1-score from highest to lowest. We describe 16 WSDDs: 6 of which were accelerometry (ACM)-based, 3 surface electromyography-based, 1 was a wearable application of EEG, 4 had multimodal sensors and 2 other types of sensors. We observed a significant inconsistency in the description of performance measures. The devices in the most advanced development phase with the highest F1-scores incorporated ACM- and sEMG-based sensors to detect tonic-clonic seizures. This review highlights the importance of implementing standards for an optimal comparison and, therefore, improving the research and development of WSDDs. WSDDs can improve the patient's care and quality of life, decrease seizure underreporting and they could potentially prevent sudden-unexpected-death in epilepsy. We discuss the central role of the neurologist in the use of WSDDs, and why a business to business to consumer model is better than the current business to consumer model of most WSDDs.
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Affiliation(s)
- Julie Verdru
- Faculty of Medicine/UZ Leuven, KU Leuven, Leuven, Belgium.
| | - Wim Van Paesschen
- Laboratory for Epilepsy Research, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
- Department of Neurology, UZ Leuven, Herestraat 49, 3000, Leuven, Belgium
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Zöllner JP, Wolking S, Weber Y, Rosenow F. [Decision support systems, assistance systems and telemedicine in epileptology]. DER NERVENARZT 2020; 92:95-106. [PMID: 33245402 PMCID: PMC7691952 DOI: 10.1007/s00115-020-01031-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 10/27/2020] [Indexed: 01/07/2023]
Abstract
Hintergrund Die wissenschaftlichen Erkenntnisse über Epilepsien und deren klinische Implikationen nehmen rasant zu. Für Nichtexperten stellt sich die zunehmende Herausforderung, den Überblick hierüber zu bewahren. Hier setzen Clinical-decision-support-Systeme (CDSS) an, indem sie standard- und expertengetriggertes Wissen zur Diagnostik und Therapie individualisiert und automatisiert liefern. Zudem sind Medizin-Apps und telemedizinische Verfahren zur Diagnostik und Therapie sowie Assistenzsysteme zur Anfallsdetektion bei Epilepsien verfügbar. Ziel der Arbeit Es soll ein Überblick über die aktuellen Entwicklungen und Anwendungsmöglichkeiten verfügbarer tele-epileptologischer Methoden gegeben werden. Material und Methoden Auf der Basis persönlicher Kenntnis und eines Literaturreviews werden epilepsiespezifische CDSS, Medizin-Apps, Assistenzsysteme sowie telemedizinische Anwendungen charakterisiert und deren klinische Einsatzmöglichkeiten dargestellt. Ergebnisse und Diskussion Personen mit Epilepsie könnten aufgrund des chronischen Verlaufs und der Komplexität der Erkrankung und ihrer Folgen von CDSS profitieren. Es erscheint wünschenswert, dass epilepsiespezifische CDSS sowohl für die Behandelnden als auch für Patienten nutzbar werden. Apps für Menschen mit Epilepsie dienen derzeit meist der Verlaufsdokumentation von Anfallsfrequenz, Medikamentencompliance und Nebenwirkungen. Gegenwärtige Anfallsdetektionssysteme erkennen vor allem generalisiert tonisch-klonische Anfälle (GTKA). Ein klinischer Nutzen ist noch nicht hinreichend belegt, erscheint aber wahrscheinlich, insbesondere da GTKA mit dem Risiko eines plötzlichen Todes von Epilepsiepatienten assoziiert sind und Interventionen als wirksam gelten.
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Affiliation(s)
- Johann Philipp Zöllner
- Epilepsiezentrum Frankfurt Rhein-Main, Zentrum der Neurologie und Neurochirurgie, Goethe-Universität Frankfurt, Schleusenweg 2-16, 60528, Frankfurt am Main, Deutschland.,LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe-Universität Frankfurt, Frankfurt am Main, 60528, Deutschland
| | - Stefan Wolking
- Epileptologie Aachen, Neurologische Uniklinik, Pauwelsstraße 30, 52074, Aachen, Deutschland
| | - Yvonne Weber
- Epileptologie Aachen, Neurologische Uniklinik, Pauwelsstraße 30, 52074, Aachen, Deutschland
| | - Felix Rosenow
- Epilepsiezentrum Frankfurt Rhein-Main, Zentrum der Neurologie und Neurochirurgie, Goethe-Universität Frankfurt, Schleusenweg 2-16, 60528, Frankfurt am Main, Deutschland. .,LOEWE Center for Personalized Translational Epilepsy Research (CePTER), Goethe-Universität Frankfurt, Frankfurt am Main, 60528, Deutschland.
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Mittlesteadt J, Bambach S, Dawes A, Wentzel E, Debs A, Sezgin E, Digby D, Huang Y, Ganger A, Bhatnagar S, Ehrenberg L, Nunley S, Glynn P, Lin S, Rust S, Patel AD. Evaluation of an Activity Tracker to Detect Seizures Using Machine Learning. J Child Neurol 2020; 35:873-878. [PMID: 32677477 DOI: 10.1177/0883073820937515] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Currently, the tracking of seizures is highly subjective, dependent on qualitative information provided by the patient and family instead of quantifiable seizure data. Usage of a seizure detection device to potentially detect seizure events in a population of epilepsy patients has been previously done. Therefore, we chose the Fitbit Charge 2 smart watch to determine if it could detect seizure events in patients when compared to continuous electroencephalographic (EEG) monitoring for those admitted to an epilepsy monitoring unit. A total of 40 patients were enrolled in the study that met the criteria between 2015 and 2016. All seizure types were recorded. Twelve patients had a total of 53 epileptic seizures. The patient-aggregated receiver operating characteristic curve had an area under the curve of 0.58 [0.56, 0.60], indicating that the neural network models were generally able to detect seizure events at an above-chance level. However, the overall low specificity implied a false alarm rate that would likely make the model unsuitable in practice. Overall, the use of the Fitbit Charge 2 activity tracker does not appear well suited in its current form to detect epileptic seizures in patients with seizure activity when compared to data recorded from the continuous EEG.
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Affiliation(s)
| | - Sven Bambach
- 51711Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Alex Dawes
- 2647The Ohio State University, Columbus, OH, USA
| | - Evelynne Wentzel
- 51711Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Andrea Debs
- 51711Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Emre Sezgin
- 51711Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Dan Digby
- 51711Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Yungui Huang
- 51711Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Andrea Ganger
- Division of Neurology, 2650Nationwide Children's Hospital, Columbus, OH, USA
| | - Shivani Bhatnagar
- Division of Neurology, 2650Nationwide Children's Hospital, Columbus, OH, USA
| | - Lori Ehrenberg
- Division of Neurology, 2650Nationwide Children's Hospital, Columbus, OH, USA
| | - Sunjay Nunley
- Prisma Health Children's Hospital and University of South Carolina School of Medicine, Greenville, SC, USA
| | - Peter Glynn
- Division of Neurology, 2650Nationwide Children's Hospital, Columbus, OH, USA
| | - Simon Lin
- 51711Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Steve Rust
- 51711Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, OH, USA
| | - Anup D Patel
- Division of Neurology, 2650Nationwide Children's Hospital, Columbus, OH, USA
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Patients self-mastery of wearable devices for seizure detection: A direct user-experience. Seizure 2020; 81:236-240. [DOI: 10.1016/j.seizure.2020.08.023] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Revised: 08/05/2020] [Accepted: 08/19/2020] [Indexed: 11/22/2022] Open
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Bernard-Willis Y, Oliveira ED, Lakhan SE. An Overview of Digital Health in the Transition of Pediatric to Adult Epilepsy Care. JOURNAL OF PEDIATRIC EPILEPSY 2020. [DOI: 10.1055/s-0040-1716825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
AbstractChildren with epilepsy often have impairments in cognitive and behavioral functioning which may hinder socio-occupational well-being as they reach adulthood. Adolescents with epilepsy have the added worry of health problems while starting the transition from family-centered pediatric care into largely autonomous adult care. If this transition is not appropriately planned and resourced, it may result in medical mistrust, nonadherence, and worsening biopsychosocial health as an adult. In recent years, there has been increased availability of digital health solutions that may be used during this stark change in care and treating teams. The digital health landscape includes a wide variety of technologies meant to address challenges faced by patients, caregivers, medical professionals, and health care systems. These technologies include mobile health products and wearable devices (e.g., seizure monitors and trackers, smartphone passive data collection), digital therapeutics (e.g., cognitive/behavioral health management; digital speech–language therapy), telehealth services (e.g., teleneurology visits), and health information technology (e.g., electronic medical records with patient portals). Such digital health solutions may empower patients in their journey toward optimal brain health during the vulnerable period of pediatric to adult care transition. Further research is needed to validate and measure their impact on clinical outcomes, health economics, and quality of life.
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Affiliation(s)
| | - Emily De Oliveira
- Department of Speech-Language Pathology, Spaulding Rehabilitation Hospital, Boston, Massachusetts, United States
| | - Shaheen E Lakhan
- Department of Biosciences, Global Neuroscience Initiative Foundation, Boston, Massachusetts, United States
- College of Science, Virginia Tech, Blacksburg, Virginia, United States
- Division of Neurology, Cambridge Health Alliance, Cambridge, Massachusetts, United States
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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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Arbune AA, Conradsen I, Cardenas DP, Whitmire LE, Voyles SR, Wolf P, Lhatoo S, Ryvlin P, Beniczky S. Ictal quantitative surface electromyography correlates with postictal EEG suppression. Neurology 2020; 94:e2567-e2576. [PMID: 32398358 PMCID: PMC7455333 DOI: 10.1212/wnl.0000000000009492] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 12/05/2019] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment. METHODS Quantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES. RESULTS We found significant correlations between quantitative surface EMG parameters and the duration of PGES (p < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%. CONCLUSIONS Ictal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk. CLASSIFICATION OF EVIDENCE This study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression.
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Affiliation(s)
- Anca A Arbune
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Isa Conradsen
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Damon P Cardenas
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Luke E Whitmire
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Shannon R Voyles
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Peter Wolf
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Samden Lhatoo
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Philippe Ryvlin
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark
| | - Sándor Beniczky
- From the Department of Clinical Neurophysiology (A.A.A., P.W., S.B.), Danish Epilepsy Centre, Dianalund, Denmark; Department of Clinical Neurosciences (A.A.A.), "Carol Davila" University of Medicine and Pharmacy, Bucharest, Romania; FORCE Technology (I.C.), Hørsholm, Denmark; Brain Sentinel (D.P.C., L.E.W., S.R.V.), San Antonio, TX; Department of Clinical Medicine (P.W.), Neurological Service, Federal University of Santa Catarina, Florianópolis, SC, Brazil; Center for SUDEP Research (S.L.), National Institute of Neurological Disorders and Stroke, Bethesda, MD; Department of Neurology (S.L.), University of Texas Health Sciences Center at Houston; Department of Clinical Neurosciences (P.R.), CHUV, Lausanne, Switzerland; Department of Clinical Neurophysiology (S.B.), Aarhus University Hospital; and Department of Clinical Medicine (S.B.), Aarhus University, Denmark.
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Zsom A, Tsekhan S, Hamid T, Levin J, Truccolo W, LaFrance WC, Blum AS, Li P, Wahed LA, Shaikh MA, Sharma G, Ranieri R, Zhang L. Ictal autonomic activity recorded via wearable-sensors plus machine learning can discriminate epileptic and psychogenic nonepileptic seizures. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2019:3502-3506. [PMID: 31946633 DOI: 10.1109/embc.2019.8857552] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Differentiating epileptic seizures (ES) and psychogenic nonepileptic seizures (PNES) is commonly based on electroencephalogram and concurrent video recordings (vEEG). Here, we demonstrate that these two types of seizures can be discriminated based on signals related to autonomic nervous system activity recorded via wearable sensors. We used Empatica E4 Wristband sensors worn on both arms in vEEG confirmed seizures, and machine learning methods to train classifiers, specifically, extreme gradient boosting (XGBoost). Classification performance achieved a predictive accuracy of 78 ± 1.5% on previously unseen data for whether a seizure was epileptic or psychogenic, which is 6 standard deviations above the baseline of 68% accuracy. Our dataset contained altogether 35 seizures from 18 patients out of which 8 patients had 13 convulsive seizures. Prediction of seizure type was based on simple features derived from the segments of autonomic activity measurements (electrodermal activity, body temperature, blood volume pulse, and heart rate) and forearm acceleration. Features related to heart rate and electrodermal activity were ranked as the top predictors in XGBoost classifiers. We found that patients with PNES had a higher ictal heart rate and electrodermal activity than patients with ES. In contrast to existing published studies of mainly convulsive seizures, our classifier focuses on autonomic signals to differentiate convulsive or nonconvulsive semiology ES from PNES. Our results show that autonomic activity recorded via wearable sensors provides promising signals for detection and discrimination of psychogenic and epileptic seizures, but more work is necessary to improve the predictive power of the model.
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Bruno E, Viana PF, Sperling MR, Richardson MP. Seizure detection at home: Do devices on the market match the needs of people living with epilepsy and their caregivers? Epilepsia 2020; 61 Suppl 1:S11-S24. [DOI: 10.1111/epi.16521] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 04/08/2020] [Accepted: 04/09/2020] [Indexed: 01/22/2023]
Affiliation(s)
- Elisa Bruno
- Division of Neuroscience Institute of Psychiatry, Psychology & Neuroscience King's College London UK
| | - Pedro F. Viana
- Division of Neuroscience Institute of Psychiatry, Psychology & Neuroscience King's College London UK
- Faculdade de Medicina Universidade de Lisboa Lisboa Portugal
- Department of Neurosciences and Mental Health (Neurology) Centro Hospitalar Lisboa Norte Lisboa Portugal
| | - Michael R. Sperling
- Department of Neurology Jefferson Comprehensive Epilepsy Center Thomas Jefferson University Philadelphia PA USA
| | - Mark P. Richardson
- Division of Neuroscience Institute of Psychiatry, Psychology & Neuroscience King's College London UK
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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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Rheims S. Wearable devices for seizure detection: Is it time to translate into our clinical practice? Rev Neurol (Paris) 2020; 176:480-484. [PMID: 32359805 DOI: 10.1016/j.neurol.2019.12.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2019] [Revised: 12/17/2019] [Accepted: 12/18/2019] [Indexed: 10/24/2022]
Abstract
With the exponential development of mobile health technologies over the past ten years, there has been a growing interest in the potential applications in the field of epilepsy, and specifically for seizure detection. Better detection of seizures is probably one of the best ways to improve patient safety. Overall, we are observing an exponential increase in the number of non-EEG based seizure detection systems and a progressive homogenization of their evaluation procedures. Most importantly, the properties of these devices for detection of tonic-clonic seizures are now very interesting, both in terms of sensitivity and in terms of false-alarm rates. Accordingly, we might expect that these be used in clinical practice in the near future, especially in patients at high risk of seizure-related injuries or sudden unexpected death in epilepsy (SUDEP).
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Affiliation(s)
- S Rheims
- Department of functional neurology and epileptology, hospices civils de Lyon, university of Lyon, Lyon, France; Inserm U1028/CNRS UMR 5292, Lyon's neuroscience research center, Lyon, France; Epilepsy institute, Lyon, France.
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Rossi KC, Joe J, Makhija M, Goldenholz DM. Insufficient Sleep, Electroencephalogram Activation, and Seizure Risk: Re-Evaluating the Evidence. Ann Neurol 2020; 87:798-806. [PMID: 32118310 DOI: 10.1002/ana.25710] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Revised: 02/27/2020] [Accepted: 02/27/2020] [Indexed: 11/10/2022]
Affiliation(s)
- Kyle C Rossi
- Department of Neurology, Division of Epilepsy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Jalyoung Joe
- Department of Neurology, Division of Epilepsy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA.,Department of Neurology, Billings Clinic, Billings, MT
| | - Monica Makhija
- Department of Neurology, Division of Epilepsy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA.,Department of Neurology, Division of Sleep Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
| | - Daniel M Goldenholz
- Department of Neurology, Division of Epilepsy, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA
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Abstract
Over the last few years, there has been significant expansion of wearable technologies and devices into the health sector, including for conditions such as epilepsy. Although there is significant potential to benefit patients, there is a paucity of well-conducted scientific research in order to inform patients and healthcare providers of the most appropriate technology. In addition to either directly or indirectly identifying seizure activity, the ideal device should improve quality of life and reduce the risk of sudden unexpected death in epilepsy (SUDEP). Devices typically monitor a number of parameters including electroencephalographic (EEG), cardiac, and respiratory patterns and can detect movement, changes in skin conductance, and muscle activity. Multimodal devices are emerging with improved seizure detection rates and reduced false positive alarms. While convulsive seizures are reliably identified by most unimodal and multimodal devices, seizures associated with no, or minimal, movement are frequently undetected. The vast majority of current devices detect but do not actively intervene. At best, therefore, they indicate the presence of seizure activity in order to accurately ascertain true seizure frequency or facilitate intervention by others, which may, nevertheless, impact the rate of SUDEP. Future devices are likely to both detect and intervene within an autonomous closed-loop system tailored to the individual and by self-learning from the analysis of patient-specific parameters. The formulation of standards for regulatory bodies to validate seizure detection devices is also of paramount importance in order to confidently ascertain the performance of a device; and this will be facilitated by the creation of a large, open database containing multimodal annotated data in order to test device algorithms. This paper is for the Special Issue: Prevent 21: SUDEP Summit - Time to Listen.
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Affiliation(s)
- Fergus Rugg-Gunn
- Dept. of Clinical and Experimental Epilepsy, National Hospital for Neurology & Neurosurgery, National Institute for Health Research (NIHR) University College London Hospitals (UCLH) Biomedical Research Centre, London, United Kingdom; Epilepsy Society Research Centre, Chalfont Centre for Epilepsy, Chalfont St Peter, Buckinghamshire, United Kingdom.
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Abstract
PURPOSE OF REVIEW There is need for automated seizure detection using mobile or wearable devices, for objective seizure documentation and decreasing morbidity and mortality associated with seizures. Due to technological development, a high number of articles have addressed non-electroencephalography (EEG)-based seizure detection. However, the quality of study-design and reporting is extremely heterogeneous. We aimed at giving the reader a clear picture on the current state of seizure detection, describing the level of evidence behind the various devices. RECENT FINDINGS Fifteen studies of phase-2 or above, demonstrated that non-EEG-based devices detected generalized tonic-clonic seizures (GTCS) with high sensitivity (≥90%) and low false alarm rate (FAR) (down to 0.2/day). We found limited evidence for detection of motor seizures other than GTCS, mostly from subgroups in larger studies, targeting GTCS. There is little evidence for non-EEG-based detection of nonmotor seizures: sensitivity is low (19-74%) with extremely high FAR (50-216/day). SUMMARY Detection of GTCS is reliable and there are several, validated devices on the market. However, detection of other seizure types needs further research.
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Abstract
Numerous technologies have been introduced for the diagnosis, treatment, and management of patients with neurologic disorders, offering the promise of early diagnosis, tailored and individualized interventions, improvement in quality of life, and restoration of neurologic function. Many of these technologies have become available commercially without having been evaluated by rigorous clinical trials and regulatory reviews, or at the least by peer review of results submitted for publication. A subset is intended to assess, assist, and monitor cognitive functions, motor skills, and autonomic functions and as such may be applicable to persons with developmental disabilities. Barriers that have previously limited the use of technologies by persons with neurodevelopmental disabilities are disappearing as new technologies that have the potential to substantially augment diagnosis and interventions to enhance the daily lives of persons with these disorders are emerging. While recent and future advances in technology have the potential to transform their lives, cautious and thoughtful evaluation is needed to ensure the technologies provide maximal value. As such, further work is needed to demonstrate feasibility, efficacy, and cost-effectiveness, and technologies should be designed to be optimized for individual use.
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Affiliation(s)
- Steven C Schachter
- Department of Neurology, Harvard Medical School, Boston, MA, United States.
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Shegog R, Braverman L, Hixson JD. Digital and technological opportunities in epilepsy: Toward a digital ecosystem for enhanced epilepsy management. Epilepsy Behav 2020; 102:106663. [PMID: 31778878 DOI: 10.1016/j.yebeh.2019.106663] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 10/29/2019] [Accepted: 10/30/2019] [Indexed: 01/01/2023]
Abstract
This commentary details the implications of a growing body of literature supporting several categories of supportive digital tools for the self-management of epilepsy. Although many prior review articles have focused on specific forms of digital epilepsy solutions, we propose the concept of an integrated self-management digital ecosystem. This would include categories of tools including self-management education programs, electronic diaries for self-monitoring, and automated wearables for seizure detection. Within these categories, individual interventions have been studied and made available to patients for years, but the evolution of a digital ecosystem promises the potential to integrate these tools in a manner that can meaningfully benefit patients' health. This commentary presents a discussion of the possible concerns that are preventing more widespread adoption of these digital health resources. Barriers are identified at multiple positions of the healthcare system, including the individual, the organizational, the community, and the policy levels.
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Affiliation(s)
- Ross Shegog
- University of Texas School of Public Health, 7000 Fannin, Suite 2668, Houston, TX 77030, United States of America
| | | | - John D Hixson
- University of California San Francisco and the San Francisco VA Medical Center, 4150 Clement Street, 127E, San Francisco, CA 94121, United States of America.
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Automated Processing of Single-Channel Surface Electromyography From Generalized Tonic–Clonic Seizures to Inform Semiology. J Clin Neurophysiol 2020; 37:56-61. [DOI: 10.1097/wnp.0000000000000618] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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