TY - JOUR
T1 - Seizure Diaries and Forecasting With Wearables
T2 - Epilepsy Monitoring Outside the Clinic
AU - Brinkmann, Benjamin H.
AU - Karoly, Philippa J.
AU - Nurse, Ewan S.
AU - Dumanis, Sonya B.
AU - Nasseri, Mona
AU - Viana, Pedro F.
AU - Schulze-Bonhage, Andreas
AU - Freestone, Dean R.
AU - Worrell, Greg
AU - Richardson, Mark P.
AU - Cook, Mark J.
N1 - Funding Information:
The authors thank the My Seizure Gauge team for technical and administrative support. Specifically the authors thank at Mayo Clinic Sherry Klingerman, Tal Pal Attia MS, Daniel Crepeau, and Erin Jagodzinski; at Seer Medical Will Hart, Dominique Eden, and Rob Kerr; at King's College London Andrea Biondi MD and Elisa Bruno MD, Ph.D.; at Freiburg University Sebastian Bottcher and Martin Glasstetter; and at the Epilepsy Foundation of America Caitlin Grazkowski Ph.D. and Jackie French MD. Funding. This work was supported by the Epilepsy Foundation of America's Epilepsy Innovation Institute My Seizure Gauge award.
Funding Information:
This work was supported by the Epilepsy Foundation of America’s Epilepsy Innovation Institute My Seizure Gauge award.
Publisher Copyright:
© Copyright © 2021 Brinkmann, Karoly, Nurse, Dumanis, Nasseri, Viana, Schulze-Bonhage, Freestone, Worrell, Richardson and Cook.
PY - 2021/7/13
Y1 - 2021/7/13
N2 - It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic–clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.
AB - It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic–clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.
KW - epilepsy
KW - machine learning
KW - multidian cycles
KW - seizure detection
KW - seizure forecasting
KW - wearable devices
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U2 - 10.3389/fneur.2021.690404
DO - 10.3389/fneur.2021.690404
M3 - Review article
AN - SCOPUS:85111575989
SN - 1664-2295
VL - 12
JO - Frontiers in Neurology
JF - Frontiers in Neurology
M1 - 690404
ER -