Abstracts

Rationale: Epitel has developed Epilog, a miniature, wireless, wearable EEG sensor. Four Epilog sensors are combined as part of Epitel’s REMI platform to create 10 channels of EEG for remote patient monitoring. REMI is designed to provide comprehensive spatial EEG recordings that can be administered by non-specialized medical personnel in any medical center. The purpose of this study was to determine how accurate epileptologists are at remotely reviewing Epilog EEG in the 10-channel REMI montage, with and without seizure detection support software.

Methods: Three board certified epileptologists reviewed Epilog EEG in the REMI montage from 20 subjects who wore four Epilog sensors for up to 5 days alongside traditional video-EEG in the EMU, 10 of whom experienced a total of 24 focal-onset electrographic seizures and 10 of whom experienced no seizures or epileptiform activity. Epileptologists randomly reviewed the same datasets with and without clinical decision support annotations from an automated seizure detection algorithm tuned to be highly sensitive.

Results: Blinded consensus review of unannotated REMI EEG detected people who were experiencing electrographic seizure activity with 90% sensitivity and 90% specificity. Consensus detection of individual focal onset seizures resulted in a mean sensitivity of 61%, precision of 80%, and false detection rate of 0.002 false positives per hour of data. With algorithm seizure detection annotations, the consensus review mean sensitivity improved to 68% with a slight increase in false detection rate (0.005 FP/hr). As standalone seizure detection software, the automated algorithm detected people who were experiencing electrographic seizure activity with 100% sensitivity and 70% specificity, and detected individual focal onset seizures with a mean sensitivity of 90% and mean false alarm rate of 0.087 FP/hr.

Conclusions: This is the first study showing epileptologists’ ability to blindly review EEG in the REMI montage from four Epilog sensors and the results demonstrate the clinical potential to accurately identify patients experiencing electrographic seizures. Additionally, the automated algorithm shows promise as clinical decision support software to detect discrete electrographic seizures in individual records as accurately as FDA-cleared predicates.

Funding: Please list any funding that was received in support of this abstract.: This work was supported in part by a grant from the National Institute of Neurological Disorders and Stroke NS100235.