Machine learning and wearable devices of the future

Beniczky, Sándor; Karoly, Philippa J.; Nurse, Ewan S.; Ryvlin, Philippe; Cook, Mark

doi:10.1111/epi.16555

Cited by 97 publications

(67 citation statements)

References 83 publications

(170 reference statements)

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“…Others have shown effects of seizure timing and seizure location on seizure occurrence 28 . Adding these data among other clinical predictors may improve performance in larger datasets in the future 29 . Furthermore, it is possible that the autonomic variability and normative ranges, which vary significantly across pediatric age groups (eg, lower resting heart rates with increasing age), may have complicated seizure forecasting.…”

Section: Discussionmentioning

confidence: 99%

Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting

et al. 2020

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Section: Discussionmentioning

confidence: 99%

Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting

et al. 2020

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“…65 Off-line analysis of the biosignals, using cloudcomputing and artificial intelligence could provide more accurate seizure detection. 66…”

Section: Recommendations For Automated Seizure Detection Using Wearmentioning

confidence: 99%

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology

et al. 2021

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The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta‐Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic‐clonic seizures (GTCS) and focal‐to‐bilateral tonic‐clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

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“…In contrast, forecasts based on “black‐box” machine learning models cannot be projected beyond the range of the available data, and therefore are less flexible for making long‐range estimates of seizure likelihood. The increasing availability of electronic diary data has recently advanced machine learning techniques for diary‐based seizure forecasting 30,44,45 . However, it is important to bear in mind that self‐reported diaries provide a noisy, undersampled representation of the underlying true seizure likelihood.…”

Section: Discussionmentioning

confidence: 99%

Cycles of self‐reported seizure likelihood correspond to yield of diagnostic epilepsy monitoring

Karoly

Eden²,

Nurse

et al. 2021

Epilepsia

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Objective Video‐electroencephalography (vEEG) is an important component of epilepsy diagnosis and management. Nevertheless, inpatient vEEG monitoring fails to capture seizures in up to one third of patients. We hypothesized that personalized seizure forecasts could be used to optimize the timing of vEEG. Methods We used a database of ambulatory vEEG studies to select a cohort with linked electronic seizure diaries of more than 20 reported seizures over at least 8 weeks. The total cohort included 48 participants. Diary seizure times were used to detect individuals' multiday seizure cycles and estimate times of high seizure risk. We compared whether estimated seizure risk was significantly different between conclusive and inconclusive vEEGs, and between vEEG with and without recorded epileptic activity. vEEGs were conducted prior to self‐reported seizures; hence, the study aimed to provide a retrospective proof of concept that cycles of seizure risk were correlated with vEEG outcomes. Results Estimated seizure risk was significantly higher for conclusive vEEGs and vEEGs with epileptic activity. Across all cycle strengths, the average time in high risk during vEEG was 29.1% compared with 14% for the conclusive/inconclusive groups and 32% compared to 18% for the epileptic activity/no epileptic activity groups. On average, 62.5% of the cohort showed increased time in high risk during their previous vEEG when epileptic activity was recorded (compared to 28% of the cohort where epileptic activity was not recorded). For conclusive vEEGs, 50% of the cohort had increased time in high risk, compared to 21.5% for inconclusive vEEGs. Significance Although retrospective, this study provides a proof of principle that scheduling monitoring times based on personalized seizure risk forecasts can improve the yield of vEEG. Forecasts can be developed at low cost from mobile seizure diaries. A simple scheduling tool to improve diagnostic outcomes may reduce cost and risks associated with delayed or missed diagnosis in epilepsy.

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Machine learning and wearable devices of the future

Cited by 97 publications

References 83 publications

Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting

Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology

Cycles of self‐reported seizure likelihood correspond to yield of diagnostic epilepsy monitoring

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