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Chen H, Wang Z, Lu C, Shu F, Chen C, Wang L, Chen W. Neonatal Seizure Detection Using a Wearable Multi-Sensor System. Bioengineering (Basel) 2023; 10:658. [PMID: 37370589 DOI: 10.3390/bioengineering10060658] [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: 03/02/2023] [Revised: 04/27/2023] [Accepted: 05/24/2023] [Indexed: 06/29/2023] Open
Abstract
Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant's movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect physiological and movement signals. In this study, we designed a second-generation multi-sensor platform and developed an automatic detection algorithm for neonatal seizures based on ECG, respiration and acceleration. Data for 38 neonates were recorded at the Children's Hospital of Fudan University in Shanghai. The total recording time was approximately 300 h. Four of the patients had seizures during data collection. The total recording time for the four patients was approximately 34 h, with 30 seizure episodes recorded. These data were evaluated by the algorithm. To evaluate the effectiveness of combining ECG, respiration and movement, we compared the performance of three types of seizure detectors. The first detector included features from ECG, respiration and acceleration records; the second detector incorporated features based on respiratory movement from respiration and acceleration records; and the third detector used only ECG-based features from ECG records. Our study illustrated that, compared with the detector utilizing individual modal features, multi-modal feature detectors could achieve favorable overall performance, reduce false alarm rates and give higher F-measures.
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Affiliation(s)
- Hongyu Chen
- Greater Bay Area Institute of Precision Medicine, Guangzhou 511466, China
| | - Zaihao Wang
- School of Information Science and Technology, Fudan University, Shanghai 200438, China
| | - Chunmei Lu
- National Health Commission Key Laboratory of Neonatal Diseases, Department of Neonatology, Children's Hospital of Fudan University, Shanghai 200433, China
| | - Feng Shu
- Collaborative Innovation Center of Polymers and Polymer Composites, Department of Macromolecular Science, Fudan University, Shanghai 201203, China
| | - Chen Chen
- School of Information Science and Technology, Fudan University, Shanghai 200438, China
| | - Laishuan Wang
- National Health Commission Key Laboratory of Neonatal Diseases, Department of Neonatology, Children's Hospital of Fudan University, Shanghai 200433, China
| | - Wei Chen
- School of Information Science and Technology, Fudan University, Shanghai 200438, China
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Automatic Detection and Classification of Epileptic Seizures in Patients with Liver Cirrhosis and Overlapping Hev Infection Based on Deep Multimodal Fusion Technology. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:3176134. [PMID: 36105452 PMCID: PMC9452993 DOI: 10.1155/2022/3176134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 07/27/2022] [Accepted: 08/06/2022] [Indexed: 11/17/2022]
Abstract
Liver cirrhosis is a clinical chronic developmental liver disease, which is caused by long-term or repeated effects of liver dysfunction, and there are more and more cases of epileptic seizures in patients with liver cirrhosis and HEV infection. This article aims to study how to analyze epileptic seizures in patients with liver cirrhosis and overlapping HEV infection based on deep multimodal fusion technology. This article proposes a deep learning neural network algorithm based on deep multimodal fusion technology, and how to use this algorithm to automatically detect and classify epileptic seizures. The data in the experiment in this article show that the prevalence of epilepsy accounts for 1% of the world's population, about 56.7 million people, and 1 in 25 people may have an epileptic seizure at some time in their lives, and in each person's life, the probability of seizures due to various reasons is 10%. In 2016, the proportion of males with cirrhosis reached 16%, females reached 8%, and males were 8% higher than females, which is a full double. The test results show that with the increase in patients with cirrhosis and overlapping HEV infection, the frequency of epileptic seizures is also getting higher and higher, indicating that the frequency of epileptic seizures has been increased in patients with cirrhosis and overlapping HEV infection. Therefore, it is imperative to analyze the epileptic seizures of patients with liver cirrhosis and overlapping HEV infection based on deep multimodal fusion technology.
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Agrahri A, Tyagi A, Kumar D, Kusumakar S, Palaniswami M, Yan B. Detection of Epileptic Seizure Using Accelerometer Time Series Data and Hidden Markov Model. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:2426-2429. [PMID: 36086544 DOI: 10.1109/embc48229.2022.9871914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Epilepsy is one of the most prevalent neurological diseases globally, which causes seizures in the patient. As per a survey done worldwide, it is found that approximately 70 million people are living with epilepsy (~1% of the total population of the world). Effective detection of these seizures requires specialized approaches such as video and electroencephalography monitoring, which are expensive and are mainly available at specialized hospitals and institutes. Hence, there is a need to develop simpler and affordable systems that can be made available to health care centers and patients for accurate detection of epileptic seizures. A wireless remote monitoring system based on a wrist-worn accelerometer is an optimum choice for the same. Sophisticated algorithms need to be developed for effectively detecting seizure events from this accelerometer data with minimal false alarms. This paper presents a Hidden Markov Model (HMM) based probabilistic approach applied to the reduced-dimension feature vector representation of time-series accelerometer data to detect epileptic seizures. The results obtained from the HMM were compared with three commonly used machine learning models viz. support vector machine (SVM), logistic regression, and random forest. The proposed approach was able to detect 95.7% of seizures with a low false alarm rate of 14.8% with a run time of just under 24 seconds.
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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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Dong C, Ye T, Long X, Aarts RM, van Dijk JP, Shang C, Liao X, Chen W, Lai W, Chen L, Wang Y. A Two-Layer Ensemble Method for Detecting Epileptic Seizures Using a Self-Annotation Bracelet With Motor Sensors. IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2022. [DOI: 10.1109/tim.2022.3173270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Chunjiao Dong
- Institute of Microelectronics of Chinese Academy of Sciences (IMECAS) and the Department of Electronic Electrical and Communication Engineering, University of Chinese Academy of Sciences (UCAS), Beijing, China
| | - Tianchun Ye
- Institute of Microelectronics of the Chinese Academy of Sciences (IMECAS), Beijing, China
| | - Xi Long
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands
| | - Ronald M. Aarts
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands
| | - Johannes P. van Dijk
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, AZ, The Netherlands
| | - Chunheng Shang
- Institute of Microelectronics of the Chinese Academy of Sciences (IMECAS), Beijing, China
| | - Xiwen Liao
- Institute of Microelectronics of the Chinese Academy of Sciences (IMECAS), Beijing, China
| | - Wei Chen
- Department of Electronic Engineering, School of Information Science and Technology, Center for Intelligent Medical Electronics, Fudan University, Shanghai, China
| | - Wanlin Lai
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Lei Chen
- Department of Neurology, West China Hospital, Sichuan University, Chengdu, China
| | - Yunfeng Wang
- Institute of Microelectronics of the Chinese Academy of Sciences (IMECAS), Beijing, China
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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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Ryvlin P, Cammoun L, Hubbard I, Ravey F, Beniczky S, Atienza D. Noninvasive detection of focal seizures in ambulatory patients. Epilepsia 2020; 61 Suppl 1:S47-S54. [PMID: 32484920 PMCID: PMC7754288 DOI: 10.1111/epi.16538] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/20/2020] [Accepted: 04/26/2020] [Indexed: 02/02/2023]
Abstract
Reliably detecting focal seizures without secondary generalization during daily life activities, chronically, using convenient portable or wearable devices, would offer patients with active epilepsy a number of potential benefits, such as providing more reliable seizure count to optimize treatment and seizure forecasting, and triggering alarms to promote safeguarding interventions. However, no generic solution is currently available to reach these objectives. A number of biosignals are sensitive to specific forms of focal seizures, in particular heart rate and its variability for seizures affecting the neurovegetative system, and accelerometry for those responsible for prominent motor activity. However, most studies demonstrate high rates of false detection or poor sensitivity, with only a minority of patients benefiting from acceptable levels of accuracy. To tackle this challenging issue, several lines of technological progress are envisioned, including multimodal biosensing with cross‐modal analytics, a combination of embedded and distributed self‐aware machine learning, and ultra–low‐power design to enable appropriate autonomy of such sophisticated portable solutions.
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Affiliation(s)
- Philippe Ryvlin
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - Leila Cammoun
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - Ilona Hubbard
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland
| | - France Ravey
- 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
| | - David Atienza
- Department of Clinical Neurosciences, Vaud University Hospital, Lausanne, Switzerland.,Embedded Systems Laboratory, Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland
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Bruno E, Böttcher S, Biondi A, Epitashvili N, Manyakov NV, Lees S, Schulze-Bonhage A, Richardson MP. Post-ictal accelerometer silence as a marker of post-ictal immobility. Epilepsia 2020; 61:1397-1405. [PMID: 32459380 DOI: 10.1111/epi.16552] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 05/06/2020] [Accepted: 05/06/2020] [Indexed: 02/04/2023]
Abstract
OBJECTIVE Movement-based wearable sensors are used for detection of convulsive seizures. The identification of the absence of motion following a seizure, known as post-ictal immobility (PI), may represent a potential additional application of wearables. PI has been associated with potentially life-threatening complications and with sudden unexpected death in epilepsy (SUDEP). We aimed to assess whether wearable accelerometers (ACCs) could be used as a digital marker of PI. METHOD Devices with embedded ACCs were worn by patients admitted to an epilepsy monitoring unit. Participants presenting with convulsive seizures were included in the study. PI presence and duration were assessed by experts reviewing video recordings. An algorithm for the automatic detection of post-ictal ACC silence and its duration was developed and the linear pairwise relationship between the automatically detected duration of post-ictal ACC silence and the duration of the expert-labeled PI was analyzed. RESULTS Twenty-two convulsive seizures were recorded from 18 study participants. Twenty were followed by PI and two by agitation. The automated estimation of post-ictal ACC silence identified all the 20 expert-labeled PI. The regression showed that the duration of the post-ictal ACC silence was correlated with the duration of PI (Pearson r = .92; P < .001), with the age of study participants (Pearson r = .78; P < .001), and with the duration of post-ictal generalized electroencephalography suppression (PGES; Pearson r = .4; P = .033). SIGNIFICANCE We highlight a novel application of wearables as a way to record post-ictal manifestations associated with an increased risk of SUDEP. The occurrence of a fatal seizure is unpredictable and the continuous, non-invasive, long-term identification of risk factors associated with each individual seizure may assume a great clinical importance.
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Affiliation(s)
- Elisa Bruno
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Sebastian Böttcher
- Epilepsy Center, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany
| | - Andrea Biondi
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| | - Nino Epitashvili
- Epilepsy Center, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany
| | - Nikolay V Manyakov
- Digital Phenotyping, Discovery Sciences, Janssen Research & Development, Beerse, Belgium
| | - Simon Lees
- The RADAR-CNS Patient Advisory Board, King's College London, London, UK
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Department of Neurosurgery, Medical Center, University of Freiburg, Freiburg, Germany
| | - Mark P Richardson
- Division of Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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Gautam C, Mishra PK, Tiwari A, Richhariya B, Pandey HM, Wang S, Tanveer M. Minimum variance-embedded deep kernel regularized least squares method for one-class classification and its applications to biomedical data. Neural Netw 2020; 123:191-216. [DOI: 10.1016/j.neunet.2019.12.001] [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: 11/20/2019] [Accepted: 12/01/2019] [Indexed: 10/25/2022]
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Schulze-Bonhage A, Böttcher S, Glasstetter M, Epitashvili N, Bruno E, Richardson M, V Laerhoven K, Dümpelmann M. [Mobile seizure monitoring in epilepsy patients]. DER NERVENARZT 2019; 90:1221-1231. [PMID: 31673723 DOI: 10.1007/s00115-019-00822-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Wearables are receiving much attention from both epilepsy patients and treating physicians, for monitoring of seizure frequency and warning of seizures. They are also of interest for the detection of seizure-associated risks of patients, for differential diagnosis of rare seizure types and prediction of seizure-prone periods. Accelerometry, electromyography (EMG), heart rate and further autonomic parameters are recorded to capture clinical seizure manifestations. Currently, a clinical use to document nocturnal motor seizures is feasible. In this review the available devices, data on the performance in the documentation of seizures, current options for clinical use and developments in data analysis are presented and critically discussed.
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Affiliation(s)
- A Schulze-Bonhage
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland.
| | - S Böttcher
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland
| | - M Glasstetter
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland
| | - N Epitashvili
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland
| | - E Bruno
- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College, London, Großbritannien
| | - M Richardson
- Institute of Psychiatry, Psychology & Neuroscience, Division of Neuroscience, King's College, London, Großbritannien
| | - K V Laerhoven
- Department Elektrotechnik und Informatik, Universität Siegen, Siegen, Deutschland
| | - M Dümpelmann
- Epilepsiezentrum, Universitätsklinikum Freiburg, Breisacher Str. 64, 79106, Freiburg, Deutschland
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Elmali AD, Bebek N, Baykan B. Let's talk SUDEP. ACTA ACUST UNITED AC 2019; 56:292-301. [PMID: 31903040 DOI: 10.29399/npa.23663] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2019] [Accepted: 05/21/2019] [Indexed: 01/17/2023]
Abstract
Sudden unexplained death in epilepsy (SUDEP) is a devastating complication of epilepsy which was under-recognized in the recent past despite its clear importance. In this review, we examine the definition of SUDEP, revise current pathophysiological theories, discuss risk factors and preventative measures, disclose tools for appraising the SUDEP risk, and last but not least dwell upon announcing and explaining the SUDEP risk to the patients and their caretakers. We aim to aid the clinicians in their responsibility of knowing SUDEP, explaining the SUDEP risk to their patients in a reasonable and sensible way and whenever possible, preventing SUDEP. Future studies are definitely needed to increase scientific knowledge and awareness related to this prioritized topic with malign consequences.
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Affiliation(s)
- Ayşe Deniz Elmali
- İstanbul University, İstanbul Faculty of Medicine, Department of Neurology, İstanbul, Turkey
| | - Nerses Bebek
- İstanbul University, İstanbul Faculty of Medicine, Department of Neurology, İstanbul, Turkey
| | - Betül Baykan
- İstanbul University, İstanbul Faculty of Medicine, Department of Neurology, İstanbul, Turkey
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Talagala PD, Hyndman RJ, Smith-Miles K, Kandanaarachchi S, Muñoz MA. Anomaly Detection in Streaming Nonstationary Temporal Data. J Comput Graph Stat 2019. [DOI: 10.1080/10618600.2019.1617160] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Affiliation(s)
- Priyanga Dilini Talagala
- Department of Econometrics and Business Statistics, Monash University, Clayton, Victoria, Australia
- ARC Centre of Excellence for Mathematics and Statistical Frontiers (ACEMS), Australia
| | - Rob J. Hyndman
- Department of Econometrics and Business Statistics, Monash University, Clayton, Victoria, Australia
- ARC Centre of Excellence for Mathematics and Statistical Frontiers (ACEMS), Australia
| | - Kate Smith-Miles
- ARC Centre of Excellence for Mathematics and Statistical Frontiers (ACEMS), Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia
| | - Sevvandi Kandanaarachchi
- Department of Econometrics and Business Statistics, Monash University, Clayton, Victoria, Australia
- ARC Centre of Excellence for Mathematics and Statistical Frontiers (ACEMS), Australia
| | - Mario A. Muñoz
- ARC Centre of Excellence for Mathematics and Statistical Frontiers (ACEMS), Australia
- School of Mathematics and Statistics, University of Melbourne, Parkville, Victoria, Australia
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Kurada AV, Srinivasan T, Hammond S, Ulate-Campos A, Bidwell J. Seizure detection devices for use in antiseizure medication clinical trials: A systematic review. Seizure 2019; 66:61-69. [PMID: 30802844 DOI: 10.1016/j.seizure.2019.02.007] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 01/28/2019] [Accepted: 02/12/2019] [Indexed: 12/25/2022] Open
Abstract
OBJECTIVE This study characterizes the current capabilities of seizure detection device (SDD) technology and evaluates the fitness of these devices for use in anti-seizure medication (ASM) clinical trials. METHODS Through a systematic literature review, 36 wireless SDDs featured in published device validation studies were identified. Each device's seizure detection capabilities that addressed ASM clinical trial primary endpoint measurement needs were cataloged. RESULTS The two most common types of seizures targeted by ASMs in clinical trials are generalized tonic-clonic (GTC) seizures and focal with impaired awareness (FIA) seizures. The Brain Sentinel SPEAC achieved the highest performance for the detection of GTC seizures (F1-score = 0.95). A non-commercial wireless EEG device achieved the highest performance for the detection of FIA seizures (F1-score = 0.88). DISCUSSION A preliminary assessment of device capabilities for measuring selected ASM clinical trial secondary endpoints was performed. The need to address key limitations in validation studies is highlighted in order to support future assessments of SDD fitness for ASM clinical trial use. In tandem, a stepwise framework to streamline device testing is put forth. These suggestions provide a starting point for establishing SDD reporting requirements before device integration into ASM clinical trials.
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Affiliation(s)
- Abhinav V Kurada
- Department of Biomedical Engineering, Columbia University School of Engineering and Applied Science, New York, NY, USA.
| | - Tarun Srinivasan
- Department of Biochemistry, Columbia University, New York, NY, USA
| | - Sarah Hammond
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Adriana Ulate-Campos
- Department of Neurology, National Children's Hospital "Dr. Carlos Saenz Herrera", San José, Costa Rica
| | - Jonathan Bidwell
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; School of Interactive Computing, Georgia Institute of Technology, 85 Fifth Street NW, Atlanta, GA, USA
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Automated Detection of Convulsive Seizures Using a Wearable Accelerometer Device. IEEE Trans Biomed Eng 2019; 66:421-432. [DOI: 10.1109/tbme.2018.2845865] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Tonic-clonic seizure detection using accelerometry-based wearable sensors: A prospective, video-EEG controlled study. Seizure 2019; 65:48-54. [DOI: 10.1016/j.seizure.2018.12.024] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2018] [Revised: 12/24/2018] [Accepted: 12/26/2018] [Indexed: 11/18/2022] Open
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Alt Murphy M, Bergquist F, Hagström B, Hernández N, Johansson D, Ohlsson F, Sandsjö L, Wipenmyr J, Malmgren K. An upper body garment with integrated sensors for people with neurological disorders - early development and evaluation. BMC Biomed Eng 2019; 1:3. [PMID: 32903336 PMCID: PMC7412666 DOI: 10.1186/s42490-019-0002-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2018] [Accepted: 01/03/2019] [Indexed: 12/23/2022] Open
Abstract
Background In neurology and rehabilitation the primary interest for using wearables is to supplement traditional patient assessment and monitoring in hospital settings with continuous data collection at home and in community settings. The aim of this project was to develop a novel wearable garment with integrated sensors designed for continuous monitoring of physiological and movement related variables to evaluate progression, tailor treatments and improve diagnosis in epilepsy, Parkinson’s disease and stroke. In this paper the early development and evaluation of a prototype designed to monitor movements and heart rate is described. An iterative development process and evaluation of an upper body garment with integrated sensors included: identification of user needs, specification of technical and garment requirements, garment development and production as well as evaluation of garment design, functionality and usability. The project is a multidisciplinary collaboration with experts from medical, engineering, textile, and material science within the wearITmed consortium. The work was organized in regular meetings, task groups and hands-on workshops. User needs were identified using results from a mixed-methods systematic review, a focus group study and expert groups. Usability was evaluated in 19 individuals (13 controls, 6 patients with Parkinson’s disease) using semi-structured interviews and qualitative content analysis. Results The garment was well accepted by the users regarding design and comfort, although the users were cautious about the technology and suggested improvements. All electronic components passed a washability test. The most robust data was obtained from accelerometer and gyroscope sensors while the electrodes for heart rate registration were sensitive to motion artefacts. The algorithm development within the wearITmed consortium has shown promising results. Conclusions The prototype was accepted by the users. Technical improvements are needed, but preliminary data indicate that the garment has potential to be used as a tool for diagnosis and treatment selection and could provide added value for monitoring seizures in epilepsy, fluctuations in PD and activity levels in stroke. Future work aims to improve the prototype further, develop algorithms, and evaluate the functionality and usability in targeted patient groups. The potential of incorporating blood pressure and heart-rate variability monitoring will also be explored.
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Affiliation(s)
- Margit Alt Murphy
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Per Dubbsgatan 14, 3rd Floor, SE-41345 Gothenburg, Sweden
| | - Filip Bergquist
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Per Dubbsgatan 14, 3rd Floor, SE-41345 Gothenburg, Sweden.,Department of Pharmacology, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Bengt Hagström
- Department of Materials, Swerea IVF, Mölndal, Sweden.,Department of Industrial and Materials Science, Chalmers University of Technology, Gothenburg, Sweden
| | - Niina Hernández
- Swedish School of Textiles, University of Borås, Borås, Sweden
| | - Dongni Johansson
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Per Dubbsgatan 14, 3rd Floor, SE-41345 Gothenburg, Sweden
| | | | - Leif Sandsjö
- MedTech West/Faculty of Caring Science, Work Life and Social Welfare, University of Borås, Borås, Sweden.,Department of Industrial and Materials Science, Division of Design & Human Factors, Chalmers University of Technology, Gothenburg, Sweden
| | | | - Kristina Malmgren
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Per Dubbsgatan 14, 3rd Floor, SE-41345 Gothenburg, Sweden.,Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
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Watkins L, Shankar R. Reducing the Risk of Sudden Unexpected Death in Epilepsy (SUDEP). Curr Treat Options Neurol 2018; 20:40. [DOI: 10.1007/s11940-018-0527-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Gutierrez EG, Crone NE, Kang JY, Carmenate YI, Krauss GL. Strategies for non-EEG seizure detection and timing for alerting and interventions with tonic-clonic seizures. Epilepsia 2018; 59 Suppl 1:36-41. [DOI: 10.1111/epi.14046] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/13/2017] [Indexed: 01/02/2023]
Affiliation(s)
| | - Nathan E. Crone
- Department of Neurology; Johns Hopkins University; Baltimore MD USA
| | - Joon Y. Kang
- Department of Neurology; Johns Hopkins University; Baltimore MD USA
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Zhao X, Lhatoo SD. Seizure detection: do current devices work? And when can they be useful? Curr Neurol Neurosci Rep 2018; 18:40. [PMID: 29796939 DOI: 10.1007/s11910-018-0849-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
PURPOSE OF REVIEW The unpredictability and apparent randomness of epileptic seizures is one of the most vexing aspects of epilepsy. Methods or devices capable of detecting seizures may help prevent injury or even death and significantly improve quality of life. Here, we summarize and evaluate currently available, unimodal, or polymodal detection systems for epileptic seizures, mainly in the ambulatory setting. RECENT FINDINGS There are two broad categories of detection devices: EEG-based and non-EEG-based systems. Wireless wearable EEG devices are now available both in research and commercial arenas. Neuro-stimulation devices are currently evolving and initial experiences of these show potential promise. As for non-EEG devices, different detecting systems show different sensitivity according to the different patient and seizure types. Regardless, when used in combination, these modalities may complement each other to increase positive predictive value. Although some devices with high sensitivity are promising, practical widespread use of such detection systems is still some way away. More research and experience are needed to evaluate the most efficient and integrated systems, to allow for better approaches to detection and prediction of seizures. The concept of closed-loop systems and prompt intervention may substantially improve quality of life for patients and carers.
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Affiliation(s)
- Xiuhe Zhao
- Epilepsy Center, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106, USA.,Neurology Department, Qilu Hospital of Shandong University, 107 Wenhuaxi Road, Jinan, 250012, Shandong Province, China
| | - Samden D Lhatoo
- Epilepsy Center, University Hospitals Cleveland Medical Center, 11100 Euclid Avenue, Cleveland, OH, 44106, USA. .,NIH/NINDS Center for SUDEP Research, Boston, MA, USA.
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21
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Affiliation(s)
- Johan B. A. M. Arends
- Academic Center for Epileptology Kempenhaeghe; Heeze The Netherlands
- Eindhoven University of Technology; Eindhoven the Netherlands
- Tele-Epilepsy Consortium; Utrecht The Netherlands
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22
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De Cooman T, Van de Vel A, Ceulemans B, Lagae L, Vanrumste B, Van Huffel S. Online detection of tonic-clonic seizures in pediatric patients using ECG and low-complexity incremental novelty detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2018; 2015:5597-600. [PMID: 26737561 DOI: 10.1109/embc.2015.7319661] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Home monitoring of refractory epilepsy patients has become of more interest the last couple of decades. A biomedical signal that can be used for online seizure detection at home is the electrocardiogram. Previous studies have shown that tonic-clonic seizures are most often accompanied with a strong heart rate increase. The main issue however is the strong patient-specific behavior of the ictal heart rate features, which makes it hard to make a patient-independent seizure detection algorithm. A patient-specific algorithm might be a solution, but existing methods require the availability of data of several seizures, which would make them inefficient in case the first seizure only occurs after a couple of days. Therefore an online method is described here that automatically converts from a patient-independent towards a patient-specific algorithm as more patient-specific data become available. This is done by using online feedback from the users to previously given alarms. By using a simplified one-class classifier, no seizure training data needs to be available for a good performance. The method is already able to adapt to the patient-specific characteristics after a couple of hours, and is able to detect 23 of 24 seizures longer than 10s, with an average of 0.38 false alarms per hour. Due to its low-complexity, it can be easily used for wearable seizure detection at home.
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23
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Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol 2018; 17:279-288. [DOI: 10.1016/s1474-4422(18)30038-3] [Citation(s) in RCA: 108] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 12/05/2017] [Accepted: 12/06/2017] [Indexed: 12/24/2022]
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Johansson D, Malmgren K, Alt Murphy M. Wearable sensors for clinical applications in epilepsy, Parkinson's disease, and stroke: a mixed-methods systematic review. J Neurol 2018; 265:1740-1752. [PMID: 29427026 PMCID: PMC6060770 DOI: 10.1007/s00415-018-8786-y] [Citation(s) in RCA: 97] [Impact Index Per Article: 16.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 02/01/2018] [Accepted: 02/02/2018] [Indexed: 01/25/2023]
Abstract
OBJECTIVES Wearable technology is increasingly used to monitor neurological disorders. The purpose of this systematic review was to synthesize knowledge from quantitative and qualitative clinical researches using wearable sensors in epilepsy, Parkinson's disease (PD), and stroke. METHODS A systematic literature search was conducted in PubMed and Scopus spanning from 1995 to January 2017. A synthesis of the main findings, reported adherence to wearables and missing data from quantitative studies, is provided. Clinimetric properties of measures derived from wearables in laboratory, free activities in hospital, and free-living environment were also evaluated. Qualitative thematic synthesis was conducted to explore user experiences and acceptance of wearables. RESULTS In total, 56 studies (50 reporting quantitative and 6 reporting qualitative data) were included for data extraction and synthesis. Among studies reporting quantitative data, 5 were in epilepsy, 21 PD, and 24 studies in stroke. In epilepsy, wearables are used to detect and differentiate seizures in hospital settings. In PD, the focus is on quantification of cardinal motor symptoms and medication-evoked adverse symptoms in both laboratory and free-living environment. In stroke upper extremity activity, walking and physical activity have been studied in laboratory and during free activities. Three analytic themes emerged from thematic synthesis of studies reporting qualitative data: acceptable integration in daily life, lack of confidence in technology, and the need to consider individualization. CONCLUSIONS Wearables may provide information of clinical features of interest in epilepsy, PD and stroke, but knowledge regarding the clinical utility for supporting clinical decision making remains to be established.
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Affiliation(s)
- Dongni Johansson
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
| | - Kristina Malmgren
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Margit Alt Murphy
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
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25
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Actigraphy: a useful tool to monitor sleep-related hypermotor seizures. Sleep Med 2017; 40:1-3. [DOI: 10.1016/j.sleep.2017.09.016] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Revised: 09/01/2017] [Accepted: 09/04/2017] [Indexed: 11/21/2022]
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26
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De Cooman T, Varon C, Hunyadi B, Van Paesschen W, Lagae L, Van Huffel S. Online Automated Seizure Detection in Temporal Lobe Epilepsy Patients Using Single-lead ECG. Int J Neural Syst 2017; 27:1750022. [DOI: 10.1142/s0129065717500228] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Automated seizure detection in a home environment has been of increased interest the last couple of decades. The electrocardiogram is one of the signals that is suited for this application. In this paper, a new method is described that classifies different heart rate characteristics in order to detect seizures from temporal lobe epilepsy patients. The used support vector machine classifier is trained on data from other patients, so that the algorithm can be used directly from the start of each new recording. The algorithm was tested on a dataset of more than 918[Formula: see text]h of data coming from 17 patients containing 127 complex partial and generalized partial seizures. The algorithm was able to detect 81.89% of the seizures, with on average 1.97 false alarms per hour. These results show a strong drop in the number of false alarms of more than 50% compared to other heart rate-based patient-independent algorithms from the literature, at the expense of a slightly higher detection delay of 17.8s on average.
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Affiliation(s)
- Thomas De Cooman
- Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10, Box 2446, Leuven, 3000, Belgium
- imec, Leuven, Belgium
| | - Carolina Varon
- Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10, Box 2446, Leuven, 3000, Belgium
- imec, Leuven, Belgium
| | - Borbála Hunyadi
- Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10, Box 2446, Leuven, 3000, Belgium
- imec, Leuven, Belgium
| | - Wim Van Paesschen
- Department of Neurology, UZ Leuven and KU Leuven, Herestraat 49, Leuven, 3000, Belgium
| | - Lieven Lagae
- Department of Child Neurology, UZ Leuven and KU Leuven, Herestraat 49, Leuven, 3000, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering, KU Leuven, Kasteelpark Arenberg 10, Box 2446, Leuven, 3000, Belgium
- imec, Leuven, Belgium
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27
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Vergara P, Villar JR, La Cal ED, Menéndez M, Sedano J. Pre-Clinical Study on the Detection of Simulated Epileptic Seizures. INT J UNCERTAIN FUZZ 2017. [DOI: 10.1142/s0218488516400092] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Wearable devices have promoted the application of Human Activity Recognition to the development of techniques for the assessment or diagnosing of illnesses and seizures, among other applications. For instance, the use of tri-axial accelerometry (3DACM) to detect abnormal and sudden movements has been introduced in the epileptic seizure recognition. In a previous research, Fuzzy Rule Based Classifiers (FRBC) have been found valid for the detection of epileptic convulsions; however, Ant Colony Systems learned FRBC performed with a high variability depending on the training data. In this study, we cope with this problem by the selection of a suitable partitioning method that has been extended to generate Fuzzy partitions. The comparison with the previous obtained results shows the fuzzy partitioning does not improve the overall performance in terms of error but highly reduces the variability in the performance of the obtained models, which allows us to obtain general models.
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Affiliation(s)
- Paula Vergara
- Computer Science Department, University of Oviedo, EIMEM c/Independencia 13, Oviedo, Asturias 33004, Spain
| | - José R. Villar
- Computer Science Department, University of Oviedo, EIMEM c/Independencia 13, Oviedo, Asturias 33004, Spain
| | - Enrique De La Cal
- Computer Science Department, University of Oviedo, EIMEM c/Independencia 13, Oviedo, Asturias 33004, Spain
| | - Manuel Menéndez
- Morphology and Cellular Biology Department, University of Oviedo, Oviedo, Asturias 33004, Spain
| | - Javier Sedano
- Applied Electronics and Artificial Intelligence, Instituto Tecnológico de Castilla y León, Pol. Ind. Villalonquéjar c/López Bravo 70, Burgos, Castilla y León 09001, Spain
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Patient-centered design criteria for wearable seizure detection devices. Epilepsy Behav 2016; 64:116-121. [PMID: 27741462 DOI: 10.1016/j.yebeh.2016.09.012] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2016] [Revised: 08/31/2016] [Accepted: 09/05/2016] [Indexed: 11/21/2022]
Abstract
INTRODUCTION Epilepsy is a common neurological condition. Seizure diary reports and patient- or caregiver-reported seizure counts are often inaccurate and underestimated. Many caregivers express stress and anxiety about the patient with epilepsy having seizures when they are not present. Therefore, a need exists for the ability to recognize and/or detect a seizure in the home setting. However, few studies have inquired on detection device features that are important to patients and their caregivers. METHODS A survey instrument utilizing a population of patients and caregivers was created to obtain information on the design criteria most desired for patients with epilepsy in regard to wearable devices. RESULTS One thousand one hundred sixty-eight responses were collected. Respondents thought that sensors for muscle signal (61.4%) and heart rate (58.0%) would be most helpful followed by the O2 sensor (41.4%). There was more interest in these three sensor types than for an accelerometer (25.5%). There was very little interest in a microphone (8.9%), galvanic skin response sensor (8.0%), or a barometer (4.9%). Based on a rating scale of 1-5 with 5 being the most important, respondents felt that "detecting all seizures" (4.73) is the most important device feature followed by "text/email alerts" (4.53), "comfort" (4.46), and "battery life" (4.43) as an equally important group of features. Respondents felt that "not knowing device is for seizures" (2.60) and "multiple uses" (2.57) were equally the least important device features. Average ratings differed significantly across age groups for the following features: button, multiuse, not knowing device is for seizures, alarm, style, and text ability. The p-values were all<0.002. Eighty-two point five percent of respondents [95% confidence interval: 80.0%, 84.7%] were willing to pay more than $100 for a wearable seizure detection device, and 42.8% of respondents [95% confidence interval: 39.8%, 45.9%] were willing to pay more than $200. CONCLUSIONS Our survey results demonstrated that patients and caregivers have design features that are important to them in regard to a wearable seizure detection device. Overall, the ability to detect all seizures rated highest among respondents which continues to be an unmet need in the community with epilepsy in regard to seizure detection. Additional uses for a wearable were not as important. Based on our results, it is important that an alert (via test and/or email) for events be a portion of the system. A reasonable price point appears to be around $200 to $300. An accelerometer was less important to those surveyed when compared with the use of heart rate, oxygen saturation, or muscle twitches/signals. As further products become developed for use in other health arenas, it will be important to consider patient and caregiver desires in order to meet the need and address the gap in devices that currently exist.
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Villar JR, Vergara P, Menéndez M, de la Cal E, González VM, Sedano J. Generalized Models for the Classification of Abnormal Movements in Daily Life and its Applicability to Epilepsy Convulsion Recognition. Int J Neural Syst 2016; 26:1650037. [PMID: 27354194 DOI: 10.1142/s0129065716500374] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The identification and the modeling of epilepsy convulsions during everyday life using wearable devices would enhance patient anamnesis and monitoring. The psychology of the epilepsy patient penalizes the use of user-driven modeling, which means that the probability of identifying convulsions is driven through generalized models. Focusing on clonic convulsions, this pre-clinical study proposes a method for generating a type of model that can evaluate the generalization capabilities. A realistic experimentation with healthy participants is performed, each with a single 3D accelerometer placed on the most affected wrist. Unlike similar studies reported in the literature, this proposal makes use of [Formula: see text] cross-validation scheme, in order to evaluate the generalization capabilities of the models. Event-based error measurements are proposed instead of classification-error measurements, to evaluate the generalization capabilities of the model, and Fuzzy Systems are proposed as the generalization modeling technique. Using this method, the experimentation compares the most common solutions in the literature, such as Support Vector Machines, [Formula: see text]-Nearest Neighbors, Decision Trees and Fuzzy Systems. The event-based error measurement system records the results, penalizing those models that raise false alarms. The results showed the good generalization capabilities of Fuzzy Systems.
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Affiliation(s)
- José R. Villar
- Computer Science Department, University of Oviedo, EIMEM, c/Independencia 13, Oviedo, Asturias 33004, Spain
| | - Paula Vergara
- Computer Science Department, University of Oviedo, EIMEM, c/Independencia 13, Oviedo, Asturias 33004, Spain
| | - Manuel Menéndez
- Cellular Morphology and Biology Department, University of Oviedo, School of Medicine, Avda. Julián Clavería 6, Oviedo, Asturias 33005, Spain
- Facultad de Ciencias de la Salud, Universidad Autónoma de Chile, Chile
| | - Enrique de la Cal
- Computer Science Department, University of Oviedo, EIMEM, c/Independencia 13, Oviedo, Asturias 33004, Spain
| | - Víctor M. González
- Control and Automation Department, University of Oviedo, Campus de Viesques, Edificio Departamental Oeste, Módulo 2, Gijón, Asturias 33204, Spain
| | - Javier Sedano
- Instituto Tecnológico de Castilla y León, Polígono Industrial Villalonquéjar. c/López Bravo, 70, Burgos, Burgos 09001, Spain
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Feature selection methods for accelerometry-based seizure detection in children. Med Biol Eng Comput 2016; 55:151-165. [PMID: 27106758 DOI: 10.1007/s11517-016-1506-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2014] [Accepted: 03/29/2016] [Indexed: 10/21/2022]
Abstract
We investigate the application of feature selection methods and their influence on distinguishing nocturnal motor seizures in epileptic children from normal nocturnal movements using accelerometry signals. We studied two feature selection methods applied one after the other to reduce the complexity and computation costs of least-squares support vector machine (LS-SVM) models. Simultaneous feature selection analyses were performed for each seizure type individually and jointly. Starting from 140 features, a filter method based on mutual information was applied to remove irrelevant and redundant features. The obtained subset was further reduced through a wrapper feature selection strategy using an LS-SVM classifier with both forward search and backward elimination. The discriminative power of each feature subset was evaluated on the test data in terms of the area under the receiver operating characteristic curve, sensitivity, and false detection rate per hour. We showed that, by using only a filter method for feature selection, it was possible to obtain classification results of comparable or slightly reduced performance with respect to the complete feature set. The attained results could facilitate further development of accelerometry-based seizure detection and alarm systems.
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Jory C, Shankar R, Coker D, McLean B, Hanna J, Newman C. Safe and sound? A systematic literature review of seizure detection methods for personal use. Seizure 2016; 36:4-15. [PMID: 26859097 DOI: 10.1016/j.seizure.2016.01.013] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2015] [Revised: 01/18/2016] [Accepted: 01/19/2016] [Indexed: 11/17/2022] Open
Abstract
PURPOSE The study aims to review systematically the quality of evidence supporting seizure detection devices. The unpredictable nature of seizures is distressing and disabling for sufferers and carers. If a seizure can be reliably detected then the patient or carer could be alerted. It could help prevent injury and death. METHODS A literature search was completed. Forty three of 120 studies found using relevant search terms were suitable for systematic review which was done applying pre-agreed criteria using PRISMA guidelines. The papers identified and reviewed were those that could have potential for everyday use of patients in a domestic setting. Studies involving long term use of scalp electrodes to record EEG were excluded on the grounds of unacceptable restriction of daily activities. RESULTS Most of the devices focused on changes in movement and/or physiological signs and were dependent on an algorithm to determine cut off points. No device was able to detect all seizures and there was an issue with both false positives and missed seizures. Many of the studies involved relatively small numbers of cases or report on only a few seizures. Reports of seizure alert dogs are also considered. CONCLUSION Seizure detection devices are at a relatively early stage of development and as yet there are no large scale studies or studies that compare the effectiveness of one device against others. The issue of false positive detection rates is important as they are disruptive for both the patient and the carer. Nevertheless, the development of seizure detection devices offers great potential in the management of epilepsy.
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Affiliation(s)
- Caryn Jory
- Cornwall Partnership NHS Foundation Trust Chy Govenek, Threemilestone Industrial Estate Truro TR4 9LD
| | - Rohit Shankar
- Cornwall Partnership NHS Foundation Trust Chy Govenek, Threemilestone Industrial Estate Truro TR4 9LD; Exeter Medical School.
| | | | | | | | - Craig Newman
- Plymouth Hospitals NHS Trust; Plymouth University
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Bidwell J, Khuwatsamrit T, Askew B, Ehrenberg JA, Helmers S. Seizure reporting technologies for epilepsy treatment: A review of clinical information needs and supporting technologies. Seizure 2015; 32:109-17. [PMID: 26552573 DOI: 10.1016/j.seizure.2015.09.006] [Citation(s) in RCA: 51] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Revised: 09/10/2015] [Accepted: 09/11/2015] [Indexed: 11/29/2022] Open
Abstract
This review surveys current seizure detection and classification technologies as they relate to aiding clinical decision-making during epilepsy treatment. Interviews and data collected from neurologists and a literature review highlighted a strong need for better distinguishing between patients exhibiting generalized and partial seizure types as well as achieving more accurate seizure counts. This information is critical for enabling neurologists to select the correct class of antiepileptic drugs (AED) for their patients and evaluating AED efficiency during long-term treatment. In our questionnaire, 100% of neurologists reported they would like to have video from patients prior to selecting an AED during an initial consultation. Presently, only 30% have access to video. In our technology review we identified that only a subset of available technologies surpassed patient self-reporting performance due to high false positive rates. Inertial seizure detection devices coupled with video capture for recording seizures at night could stand to address collecting seizure counts that are more accurate than current patient self-reporting during day and night time use.
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Affiliation(s)
- Jonathan Bidwell
- School of Interactive Computing, Georgia Institute of Technology, 85 Fifth Street NW Atlanta, GA, USA.
| | - Thanin Khuwatsamrit
- School of Interactive Computing, Georgia Institute of Technology, 85 Fifth Street NW Atlanta, GA, USA
| | - Brittain Askew
- School of Medicine, Emory University, 1648 Pierce Dr NE, Atlanta, GA, USA
| | | | - Sandra Helmers
- School of Medicine, Emory University, 1648 Pierce Dr NE, Atlanta, GA, USA
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Milosevic M, Van de Vel A, Bonroy B, Ceulemans B, Lagae L, Vanrumste B, Huffel SV. Automated Detection of Tonic-Clonic Seizures Using 3-D Accelerometry and Surface Electromyography in Pediatric Patients. IEEE J Biomed Health Inform 2015; 20:1333-1341. [PMID: 26241981 DOI: 10.1109/jbhi.2015.2462079] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Epileptic seizure detection is traditionally done using video/electroencephalography monitoring, which is not applicable for long-term home monitoring. In recent years, attempts have been made to detect the seizures using other modalities. In this study, we investigated the application of four accelerometers (ACM) attached to the limbs and surface electromyography (sEMG) electrodes attached to upper arms for the detection of tonic-clonic seizures. sEMG can identify the tension during the tonic phase of tonic-clonic seizure, while ACM is able to detect rhythmic patterns of the clonic phase of tonic-clonic seizures. Machine learning techniques, including feature selection and least-squares support vector machine classification, were employed for detection of tonic-clonic seizures from ACM and sEMG signals. In addition, the outputs of ACM and sEMG-based classifiers were combined using a late integration approach. The algorithms were evaluated on 1998.3 h of data recorded nocturnally in 56 patients of which seven had 22 tonic-clonic seizures. A multimodal approach resulted in a more robust detection of short and nonstereotypical seizures (91%), while the number of false alarms increased significantly compared with the use of single sEMG modality (0.28-0.5/12h). This study also showed that the choice of the recording system should be made depending on the prevailing pediatric patient-specific seizure characteristics and nonepileptic behavior.
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Affiliation(s)
- Milica Milosevic
- Department of Electrical Engineering (ESAT), STADIUS, KU Leuven and iMinds IT Department, Leuven, Belgium
| | - Anouk Van de Vel
- Department of Neurology-Paediatric Neurology, University Hospital University of Antwerp, Wilrijk, Belgium
| | | | - Berten Ceulemans
- Rehabilitation Center for Children and Youth Pulderbos, Pulderbos, Belgium
| | - Lieven Lagae
- Department of Child Neurology, University Hospital KU Leuven, Leuven, Belgium
| | - Bart Vanrumste
- Department of Electrical Engineering (ESAT), Advanced Integrated Sensing (AdvISe), KU Leuven, Geel, Belgium
| | - Sabine Van Huffel
- Department of Electrical Engineering (ESAT), STADIUS, KU Leuven and iMinds IT Department, Leuven, Belgium
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Gubbi J, Kusmakar S, Rao AS, Yan B, OBrien T, Palaniswami M. Automatic Detection and Classification of Convulsive Psychogenic Nonepileptic Seizures Using a Wearable Device. IEEE J Biomed Health Inform 2015; 20:1061-72. [PMID: 26087511 DOI: 10.1109/jbhi.2015.2446539] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Epilepsy is one of the most common neurological disorders and patients suffer from unprovoked seizures. In contrast, psychogenic nonepileptic seizures (PNES) are another class of seizures that are involuntary events not caused by abnormal electrical discharges but are a manifestation of psychological distress. The similarity of these two types of seizures poses diagnostic challenges that often leads in delayed diagnosis of PNES. Further, the diagnosis of PNES involves high-cost hospital admission and monitoring using video-electroencephalogram machines. A wearable device that can monitor the patient in natural setting is a desired solution for diagnosis of convulsive PNES. A wearable device with an accelerometer sensor is proposed as a new solution in the detection and diagnosis of PNES. The seizure detection algorithm and PNES classification algorithm are developed. The developed algorithms are tested on data collected from convulsive epileptic patients. A very high seizure detection rate is achieved with 100% sensitivity and few false alarms. A leave-one-out error of 6.67% is achieved in PNES classification, demonstrating the usefulness of wearable device in the diagnosis of PNES.
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