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Article ; Online: Minimum clinical utility standards for wearable seizure detectors: A simulation study.

Goldenholz, Daniel M / Karoly, Philippa J / Viana, Pedro F / Nurse, Ewan / Loddenkemper, Tobias / Schulze-Bonhage, Andreas / Vieluf, Solveig / Bruno, Elisa / Nasseri, Mona / Richardson, Mark P / Brinkmann, Benjamin H / Westover, M Brandon

Epilepsia

2024 Volume 65, Issue 4, Page(s) 1017–1028

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 ... ...

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.
MeSH term(s)	Humans ; Sudden Unexpected Death in Epilepsy/prevention & control ; Seizures/diagnosis ; Seizures/therapy ; Epilepsy/diagnosis ; Wearable Electronic Devices ; Epilepsy, Generalized ; Electroencephalography/methods
Language	English
Publishing date	2024-02-17
Publishing country	United States
Document type	Journal Article
ZDB-ID	216382-2
ISSN	1528-1167 ; 0013-9580
ISSN (online)	1528-1167
ISSN	0013-9580
DOI	10.1111/epi.17917
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Zs.A 517: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: Machine learning and wearable devices of the future.

Beniczky, Sándor / Karoly, Philippa / Nurse, Ewan / Ryvlin, Philippe / Cook, Mark

Epilepsia

2020 Volume 62 Suppl 2, Page(s) S116–S124

Abstract: Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all ...

Abstract	Machine learning (ML) is increasingly recognized as a useful tool in healthcare applications, including epilepsy. One of the most important applications of ML in epilepsy is seizure detection and prediction, using wearable devices (WDs). However, not all currently available algorithms implemented in WDs are using ML. In this review, we summarize the state of the art of using WDs and ML in epilepsy, and we outline future development in these domains. There is published evidence for reliable detection of epileptic seizures using implanted electroencephalography (EEG) electrodes and wearable, non-EEG devices. Application of ML using the data recorded with WDs from a large number of patients could change radically the way we diagnose and manage patients with epilepsy.
Language	English
Publishing date	2020-07-26
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	216382-2
ISSN	1528-1167 ; 0013-9580
ISSN (online)	1528-1167
ISSN	0013-9580
DOI	10.1111/epi.16555
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Zs.A 517: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article: Pattern recognition as a guide in diagnosing headache.

Nurse, E G

Canadian family physician Medecin de famille canadien

2010 Volume 20, Issue 12, Page(s) 55–61

Abstract: This paper presents the family physician with a comprehensive yet simple classification that is easily followed. By a system known as 'pattern recognition', the physician may determine cause in the majority of headaches in his own office. With use of ... ...

Abstract	This paper presents the family physician with a comprehensive yet simple classification that is easily followed. By a system known as 'pattern recognition', the physician may determine cause in the majority of headaches in his own office. With use of ancillary investigatory procedures where necessary, he will refer only when the diagnosis warrants it.
Language	English
Publishing date	2010-05-01
Publishing country	Canada
Document type	Journal Article
ZDB-ID	603565-6
ISSN	0008-350X
ISSN	0008-350X
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Article: Perceived seizure risk in epilepsy â€" Chronic electronic surveys with and without concurrent EEG.

Cui, Jie / Balzekas, Irena / Nurse, Ewan / Viana, Pedro / Gregg, Nicholas / Karoly, Philippa / Worrell, Gregory / Richardson, Mark P / Freestone, Dean R / Brinkmann, Benjamin H

medRxiv : the preprint server for health sciences

2023

Abstract: Objective: Previous studies suggested that patients with epilepsy might be able to fore-cast their own seizures. We sought to assess the relationships of premonitory symptoms and perceived seizure risk with future and recent self-reported and EEG- ... ...

Abstract	Objective: Previous studies suggested that patients with epilepsy might be able to fore-cast their own seizures. We sought to assess the relationships of premonitory symptoms and perceived seizure risk with future and recent self-reported and EEG-confirmed seizures in the subjects living with epilepsy in their natural home environments. Methods: We collected long-term e-surveys from ambulatory patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication compliance, sleep quality, mood, stress, perceived seizure risk and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with device seizure forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). Results: Sixty-nine subjects returned 12,590 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed increased stress (OR = 2.52, 95% CI = [1.52, 4.14], Significance: It appears that patients may tend to self-forecast seizures that occur in sequential groupings. Our results suggest that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting. Key points: Long-term e-surveys data and concurrent EEG signals were collected across three study sites to assess the ability of the patients to self-forecast their seizures.Patients may tend to self-forecast self-reported seizures that occur in sequential groupings.Factors, such as mood and stress, may not be independent premonitory symptoms but may be the consequence of recent seizures.No ability to self-forecast EEG confirmed seizures was observed in a small cohort with concurrent EEG validation.A mathematic relation between OR and AUC provides a means to compare forecasting performance between survey and device studies.
Language	English
Publishing date	2023-03-28
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.03.23.23287561
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Article ; Online: Perceived seizure risk in epilepsy: Chronic electronic surveys with and without concurrent electroencephalography.

Cui, Jie / Balzekas, Irena / Nurse, Ewan / Viana, Pedro / Gregg, Nicholas / Karoly, Philippa / Stirling, Rachel E / Worrell, Gregory / Richardson, Mark P / Freestone, Dean R / Brinkmann, Benjamin H

Epilepsia

2023 Volume 64, Issue 9, Page(s) 2421–2433

Abstract: Objective: Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self-reported and ... ...

Abstract	Objective: Previous studies suggested that patients with epilepsy might be able to forecast their own seizures. This study aimed to assess the relationships between premonitory symptoms, perceived seizure risk, and future and recent self-reported and electroencephalographically (EEG)-confirmed seizures in ambulatory patients with epilepsy in their natural home environments. Methods: Long-term e-surveys were collected from patients with and without concurrent EEG recordings. Information obtained from the e-surveys included medication adherence, sleep quality, mood, stress, perceived seizure risk, and seizure occurrences preceding the survey. EEG seizures were identified. Univariate and multivariate generalized linear mixed-effect regression models were used to estimate odds ratios (ORs) for the assessment of the relationships. Results were compared with the seizure forecasting classifiers and device forecasting literature using a mathematical formula converting OR to equivalent area under the curve (AUC). Results: Fifty-four subjects returned 10 269 e-survey entries, with four subjects acquiring concurrent EEG recordings. Univariate analysis revealed that increased stress (OR = 2.01, 95% confidence interval [CI] = 1.12-3.61, AUC = .61, p = .02) was associated with increased relative odds of future self-reported seizures. Multivariate analysis showed that previous self-reported seizures (OR = 5.37, 95% CI = 3.53-8.16, AUC = .76, p < .001) were most strongly associated with future self-reported seizures, and high perceived seizure risk (OR = 3.34, 95% CI = 1.87-5.95, AUC = .69, p < .001) remained significant when prior self-reported seizures were added to the model. No correlation with medication adherence was found. No significant association was found between e-survey responses and subsequent EEG seizures. Significance: Our results suggest that patients may tend to self-forecast seizures that occur in sequential groupings and that low mood and increased stress may be the result of previous seizures rather than independent premonitory symptoms. Patients in the small cohort with concurrent EEG showed no ability to self-predict EEG seizures. The conversion from OR to AUC values facilitates direct comparison of performance between survey and device studies involving survey premonition and forecasting.
MeSH term(s)	Humans ; Seizures/diagnosis ; Seizures/epidemiology ; Epilepsy/complications ; Epilepsy/diagnosis ; Epilepsy/epidemiology ; Electroencephalography/methods ; Multivariate Analysis ; Surveys and Questionnaires
Language	English
Publishing date	2023-06-19
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	216382-2
ISSN	1528-1167 ; 0013-9580
ISSN (online)	1528-1167
ISSN	0013-9580
DOI	10.1111/epi.17678
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Zs.A 517: Show issues			Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (1.OG) ab Jg. 2022: Lesesaal (EG)
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Article ; Online: A cross-sectional investigation of cognition and epileptiform discharges in juvenile absence epilepsy.

Dharan, Anita L / Bowden, Stephen C / Peterson, Andre / Lai, Alan / Seneviratne, Udaya / Dabscheck, Gabriel / Nurse, Ewan / Loughman, Amy / Parsons, Nicholas / D'Souza, Wendyl J

Epilepsia

2023 Volume 64, Issue 3, Page(s) 742–753

Abstract: Objectives: Despite the prevalence of cognitive symptoms in the idiopathic generalized epilepsies (IGEs), cognitive dysfunction in juvenile absence epilepsy (JAE), a common yet understudied IGE subtype, remains poorly understood. This descriptive study ... ...

Abstract	Objectives: Despite the prevalence of cognitive symptoms in the idiopathic generalized epilepsies (IGEs), cognitive dysfunction in juvenile absence epilepsy (JAE), a common yet understudied IGE subtype, remains poorly understood. This descriptive study provides a novel, comprehensive characterization of cognitive functioning in a JAE sample and examines the relationship between cognition and 24-h epileptiform discharge load. Method: Forty-four individuals diagnosed with JAE underwent cognitive assessment using Woodcock Johnson III Test of Cognitive Abilities with concurrent 24-h ambulatory EEG monitoring. Generalized epileptiform discharges of any length, and prolonged generalized discharges ≥3 s were quantified across wakefulness and sleep. The relationship between standardized cognitive scores and epileptiform discharges was assessed through regression models. Results: Cognitive performances in overall intellectual ability, acquired comprehension-knowledge, processing speed, long-term memory storage and retrieval, and executive processes were 0.63-1.07 standard deviation (SD) units lower in the JAE group compared to the population reference mean, adjusted for educational attainment. Prolonged discharges (≥3 s) were recorded in 20 patients (47.6%) from 42 available electroencephalography (EEG) studies and were largely unreported. Duration and number of prolonged discharges were associated with reduced processing speed and long-term memory storage and retrieval. Significance: Cognitive dysfunction is seen in patients with JAE across various cognitive abilities, including those representing more stable processes like general intellect. During 24-h EEG, prolonged epileptiform discharges are common yet underreported in JAE despite treatment, and they show moderate effects on cognitive abilities. If epileptiform burden is a modifiable predictor of cognitive dysfunction, therapeutic interventions should consider quantitative 24-h EEG with routine neuropsychological screening. The growing recognition of the spectrum of neuropsychological comorbidities of IGE highlights the value of multidisciplinary approaches to explore the causes and consequences of cognitive deficits in epilepsy.
MeSH term(s)	Humans ; Epilepsy, Absence ; Cross-Sectional Studies ; Electroencephalography ; Cognition ; Immunoglobulin E
Chemical Substances	Immunoglobulin E (37341-29-0)
Language	English
Publishing date	2023-02-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	216382-2
ISSN	1528-1167 ; 0013-9580
ISSN (online)	1528-1167
ISSN	0013-9580
DOI	10.1111/epi.17505
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Article ; Online: Feasibility and acceptability of an ultra-long-term at-home EEG monitoring system (EEG@HOME) for people with epilepsy.

Biondi, Andrea / Simblett, Sara K / Viana, Pedro F / Laiou, Petroula / Fiori, Anna M G / Nurse, Ewan / Schreuder, Martijn / Pal, Deb K / Richardson, Mark P

Epilepsy & behavior : E&B

2023 Volume 151, Page(s) 109609

Abstract: Background: Recent technological advancements offer new ways to monitor and manage epilepsy. The adoption of these devices in routine clinical practice will strongly depend on patient acceptability and usability, with their perspectives being crucial. ... ...

Abstract	Background: Recent technological advancements offer new ways to monitor and manage epilepsy. The adoption of these devices in routine clinical practice will strongly depend on patient acceptability and usability, with their perspectives being crucial. Previous studies provided feedback from patients, but few explored the experience of them using independently multiple devices independently at home. Purpose: The study, assessed through a mixed methods design, the direct experiences of people with epilepsy independently using a non-invasive monitoring system (EEG@HOME) for an extended duration of 6 months, at home. We aimed to investigate factors affecting engagement, gather qualitative insights, and provide recommendations for future home epilepsy monitoring systems. Materials and methods: Adults with epilepsy independently were trained to use a wearable dry EEG system, a wrist-worn device, and a smartphone app for seizure tracking and behaviour monitoring for 6 months at home. Monthly acceptability questionnaires (PSSUQ, SUS) and semi-structured interviews were conducted to explore participant experience. Adherence with the procedure, acceptability scores and systematic thematic analysis of the interviews, focusing on the experience with the procedure, motivation and benefits and opinion about the procedure were assessed. Results: Twelve people with epilepsy took part into the study for an average of 193.8 days (range 61 to 312) with a likelihood of using the system at six months of 83 %. The e-diary and the smartwatch were highly acceptable and preferred to a wearable EEG system (PSSUQ score of 1.9, 1.9, 2.4). Participants showed an acceptable level of adherence with all solutions (Average usage of 63 %, 66 %, 92 %) reporting more difficulties using the EEG twice a day and remembering to complete the daily behavioural questionnaires. Clear information and training, continuous remote support, perceived direct and indirect benefits and the possibility to have a flexible, tailored to daily routine monitoring were defined as key factors to ensure compliance with long-term monitoring systems. Conclusions: EEG@HOME study demonstrated people with epilepsy' interest and ability in active health monitoring using new technologies. Remote training and support enable independent home use of new non-invasive technologies, but to ensure long term acceptability and usability systems will require to be integrated into patients' routines, include healthcare providers, and offer continuous support and personalized feedback.
MeSH term(s)	Adult ; Humans ; Feasibility Studies ; Epilepsy/diagnosis ; Health Personnel ; Surveys and Questionnaires ; Electroencephalography
Language	English
Publishing date	2023-12-30
Publishing country	United States
Document type	Journal Article
ZDB-ID	2010587-3
ISSN	1525-5069 ; 1525-5050
ISSN (online)	1525-5069
ISSN	1525-5050
DOI	10.1016/j.yebeh.2023.109609
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Article: Remote and Long-Term Self-Monitoring of Electroencephalographic and Noninvasive Measurable Variables at Home in Patients With Epilepsy (EEG@HOME): Protocol for an Observational Study.

Biondi, Andrea / Laiou, Petroula / Bruno, Elisa / Viana, Pedro F / Schreuder, Martijn / Hart, William / Nurse, Ewan / Pal, Deb K / Richardson, Mark P

JMIR research protocols

2021 Volume 10, Issue 3, Page(s) e25309

Abstract: Background: Epileptic seizures are spontaneous events that severely affect the lives of patients due to their recurrence and unpredictability. The integration of new wearable and mobile technologies to collect electroencephalographic (EEG) and ... ...

Abstract	Background: Epileptic seizures are spontaneous events that severely affect the lives of patients due to their recurrence and unpredictability. The integration of new wearable and mobile technologies to collect electroencephalographic (EEG) and extracerebral signals in a portable system might be the solution to prospectively identify times of seizure occurrence or propensity. The performances of several seizure detection devices have been assessed by validated studies, and patient perspectives on wearables have been explored to better match their needs. Despite this, there is a major gap in the literature on long-term, real-life acceptability and performance of mobile technology essential to managing chronic disorders such as epilepsy. Objective: EEG@HOME is an observational, nonrandomized, noninterventional study that aims to develop a new feasible procedure that allows people with epilepsy to independently, continuously, and safely acquire noninvasive variables at home. The data collected will be analyzed to develop a general model to predict periods of increased seizure risk. Methods: A total of 12 adults with a diagnosis of pharmaco-resistant epilepsy and at least 20 seizures per year will be recruited at King's College Hospital, London. Participants will be asked to self-apply an easy and portable EEG recording system (ANT Neuro) to record scalp EEG at home twice daily. From each serial EEG recording, brain network ictogenicity (BNI), a new biomarker of the propensity of the brain to develop seizures, will be extracted. A noninvasive wrist-worn device (Fitbit Charge 3; Fitbit Inc) will be used to collect non-EEG biosignals (heart rate, sleep quality index, and steps), and a smartphone app (Seer app; Seer Medical) will be used to collect data related to seizure occurrence, medication taken, sleep quality, stress, and mood. All data will be collected continuously for 6 months. Standardized questionnaires (the Post-Study System Usability Questionnaire and System Usability Scale) will be completed to assess the acceptability and feasibility of the procedure. BNI, continuous wrist-worn sensor biosignals, and electronic survey data will be correlated with seizure occurrence as reported in the diary to investigate their potential values as biomarkers of seizure risk. Results: The EEG@HOME project received funding from Epilepsy Research UK in 2018 and was approved by the Bromley Research Ethics Committee in March 2020. The first participants were enrolled in October 2020, and we expect to publish the first results by the end of 2022. Conclusions: With the EEG@HOME study, we aim to take advantage of new advances in remote monitoring technology, including self-applied EEG, to investigate the feasibility of long-term disease self-monitoring. Further, we hope our study will bring new insights into noninvasively collected personalized risk factors of seizure occurrence and seizure propensity that may help to mitigate one of the most difficult aspects of refractory epilepsy: the unpredictability of seizure occurrence. International registered report identifier (irrid): PRR1-10.2196/25309.
Language	English
Publishing date	2021-03-19
Publishing country	Canada
Document type	Journal Article
ZDB-ID	2719222-2
ISSN	1929-0748
ISSN	1929-0748
DOI	10.2196/25309
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Article ; Online: Sleep and seizure risk in epilepsy: bed and wake times are more important than sleep duration.

Stirling, Rachel E / Hidajat, Cindy M / Grayden, David B / D'Souza, Wendyl J / Naim-Feil, Jodie / Dell, Katrina L / Schneider, Logan D / Nurse, Ewan / Freestone, Dean / Cook, Mark J / Karoly, Philippa J

Brain : a journal of neurology

2022 Volume 146, Issue 7, Page(s) 2803–2813

Abstract: Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent ... ...

Abstract	Sleep duration, sleep deprivation and the sleep-wake cycle are thought to play an important role in the generation of epileptic activity and may also influence seizure risk. Hence, people diagnosed with epilepsy are commonly asked to maintain consistent sleep routines. However, emerging evidence paints a more nuanced picture of the relationship between seizures and sleep, with bidirectional effects between changes in sleep and seizure risk in addition to modulation by sleep stages and transitions between stages. We conducted a longitudinal study investigating sleep parameters and self-reported seizure occurrence in an ambulatory at-home setting using mobile and wearable monitoring. Sixty subjects wore a Fitbit smartwatch for at least 28 days while reporting their seizure activity in a mobile app. Multiple sleep features were investigated, including duration, oversleep and undersleep, and sleep onset and offset times. Sleep features in participants with epilepsy were compared to a large (n = 37 921) representative population of Fitbit users, each with 28 days of data. For participants with at least 10 seizure days (n = 34), sleep features were analysed for significant changes prior to seizure days. A total of 4956 reported seizures (mean = 83, standard deviation = 130) and 30 485 recorded sleep nights (mean = 508, standard deviation = 445) were included in the study. There was a trend for participants with epilepsy to sleep longer than the general population, although this difference was not significant. Just 5 of 34 participants showed a significant difference in sleep duration the night before seizure days compared to seizure-free days. However, 14 of 34 subjects showed significant differences between their sleep onset (bed) and/or offset (wake) times before seizure occurrence. In contrast to previous studies, the current study found undersleeping was associated with a marginal 2% decrease in seizure risk in the following 48 h (P < 0.01). Nocturnal seizures were associated with both significantly longer sleep durations and increased risk of a seizure occurring in the following 48 h. Overall, the presented results demonstrated that day-to-day changes in sleep duration had a minimal effect on reported seizures, while patient-specific changes in bed and wake times were more important for identifying seizure risk the following day. Nocturnal seizures were the only factor that significantly increased the risk of seizures in the following 48 h on a group level. Wearables can be used to identify these sleep-seizure relationships and guide clinical recommendations or improve seizure forecasting algorithms.
MeSH term(s)	Humans ; Sleep Duration ; Longitudinal Studies ; Electroencephalography ; Sleep ; Epilepsy/complications ; Epilepsy/epidemiology ; Seizures/complications
Language	English
Publishing date	2022-12-09
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	80072-7
ISSN	1460-2156 ; 0006-8950
ISSN (online)	1460-2156
ISSN	0006-8950
DOI	10.1093/brain/awac476
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Article: Forecasting Seizure Likelihood With Wearable Technology.

Stirling, Rachel E / Grayden, David B / D'Souza, Wendyl / Cook, Mark J / Nurse, Ewan / Freestone, Dean R / Payne, Daniel E / Brinkmann, Benjamin H / Pal Attia, Tal / Viana, Pedro F / Richardson, Mark P / Karoly, Philippa J

Frontiers in neurology

2021 Volume 12, Page(s) 704060

Abstract: The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated with seizures but ... ...

Abstract	The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated with seizures but need to be feasible and accessible in the long-term. Wearable devices are perfect candidates to develop non-invasive, accessible forecasts but are yet to be investigated in long-term studies. We hypothesized that machine learning models could utilize heart rate as a biomarker for well-established cycles of seizures and epileptic activity, in addition to other wearable signals, to forecast high and low risk seizure periods. This feasibility study tracked participants' (
Language	English
Publishing date	2021-07-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2564214-5
ISSN	1664-2295
ISSN	1664-2295
DOI	10.3389/fneur.2021.704060
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