Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models

• Published in: Epilepsia (Copenhagen) Vol. 64; no. S4; pp. S124 - S133
• Main Authors: Viana, Pedro, Pal Attia, Tal, Nasseri, Mona, Duun‐Henriksen, Jonas, Biondi, Andrea, Winston, Joel S., Pavão Martins, Isabel, Nurse, Ewan S., Dümpelmann, Matthias, Schulze‐Bonhage, Andreas, Freestone, Dean R., Kjaer, Troels W., Richardson, Mark P., Brinkmann, Benjamin H.
• Format: Journal Article
• Language: English
• Published: United States Wiley 01.12.2023; Wiley Subscription Services, Inc
• Subjects: Circadian rhythms; Convulsions & seizures; Deep learning; Drug Resistant Epilepsy - diagnosis; EEG; Electroencephalography; Epilepsy; Forecasting; Humans; Mobile health; Periodicity; Prospective Studies; ROC Curve; Seizure forecasting; Seizure prediction; Seizures; Seizures - diagnosis; Subcutaneous EEG; epilepsy; deep learning; seizure prediction; mobile health; subcutaneous EEG; seizure forecasting
• ISSN: 0013-9580; 1528-1167; 1528-1167
• DOI: 10.1111/epi.17252

© 2022 International League Against Epilepsy Objective: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. Methods: We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. Results: Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. Significance: This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG. This work was supported by the Epilepsy Foundation’s Epilepsy Innovation Institute My Seizure Gauge Project. M.P.R. is supported by the NIHR Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust, the MRC Centre for Neurodevelopmental Disorders (MR/N026063/1), and the RADAR-CNS project funded by the European Commission (www.radar-cns.org, grant agreement 115902). B.H.B. is supported by the Mayo Neurology AI Program and by the NIH (NS UG3 123066).