  Objective: Most current wearable tonic-clonic seizure (TCS) detection systems
are based on extra-cerebral signals, such as electromyography (EMG) or
accelerometry (ACC). Although many of these devices show good sensitivity in
seizure detection, their false positive rates (FPR) are still relatively high.
Wearable EEG may improve performance; however, studies investigating this
remain scarce. This paper aims 1) to investigate the possibility of detecting
TCSs with a behind-the-ear, two-channel wearable EEG, and 2) to evaluate the
added value of wearable EEG to other non-EEG modalities in multimodal TCS
detection. Method: We included 27 participants with a total of 44 TCSs from the
European multicenter study SeizeIT2. The multimodal wearable detection system
Sensor Dot (Byteflies) was used to measure two-channel, behind-the-ear EEG,
EMG, electrocardiography (ECG), ACC and gyroscope (GYR). First, we evaluated
automatic unimodal detection of TCSs, using performance metrics such as
sensitivity, precision, FPR and F1-score. Secondly, we fused the different
modalities and again assessed performance. Algorithm-labeled segments were then
provided to a neurologist and a wearable data expert, who reviewed and
annotated the true positive TCSs, and discarded false positives (FPs). Results:
Wearable EEG outperformed the other modalities in unimodal TCS detection by
achieving a sensitivity of 100.0% and a FPR of 10.3/24h (compared to 97.7%
sensitivity and 30.9/24h FPR for EMG; 95.5% sensitivity and 13.9 FPR for ACC).
The combination of wearable EEG and EMG achieved overall the most clinically
useful performance in offline TCS detection with a sensitivity of 97.7%, a FPR
of 0.4/24 h, a precision of 43.0%, and a F1-score of 59.7%. Subsequent visual
review of the automated detections resulted in maximal sensitivity and zero
FPs.
