Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection

Physiol Meas. 2024 Jun 7;45(6). doi: 10.1088/1361-6579/ad4e94.

Abstract

Objective. This paper aims to investigate the possibility of detecting tonic-clonic seizures (TCSs) with behind-the-ear, two-channel wearable electroencephalography (EEG), and to evaluate its added value to non-EEG modalities in TCS detection.Methods. We included 27 participants with a total of 44 TCSs from the European multicenter study SeizeIT2. The wearable Sensor Dot (Byteflies) was used to measure behind-the-ear EEG, electromyography (EMG), electrocardiography, accelerometry (ACC) and gyroscope. We evaluated automatic unimodal detection of TCSs, using sensitivity, precision, false positive rate (FPR) and F1-score. Subsequently, we fused the different modalities and again assessed performance. Algorithm-labeled segments were then provided to two experts, who annotated true positive TCSs, and discarded false positives.Results. Wearable EEG outperformed the other single modalities with a sensitivity of 100% and a FPR of 10.3/24 h. The combination of wearable EEG and EMG proved most clinically useful, delivering a sensitivity of 97.7%, an FPR of 0.4/24 h, a precision of 43%, and an F1-score of 59.7%. The highest overall performance was achieved through the fusion of wearable EEG, EMG, and ACC, yielding a sensitivity of 90.9%, an FPR of 0.1/24 h, a precision of 75.5%, and an F1-score of 82.5%.Conclusions. In TCS detection with a wearable device, combining EEG with EMG, ACC or both resulted in a remarkable reduction of FPR, while retaining a high sensitivity.Significance. Adding wearable EEG could further improve TCS detection, relative to extracerebral-based systems.

Keywords: epilepsy; multimodal seizure detection; tonic-clonic seizure; wearable EEG.

MeSH terms

  • Accelerometry* / instrumentation
  • Adult
  • Electroencephalography* / instrumentation
  • Electroencephalography* / methods
  • Electromyography* / instrumentation
  • Female
  • Humans
  • Male
  • Middle Aged
  • Seizures* / diagnosis
  • Seizures* / physiopathology
  • Signal Processing, Computer-Assisted*
  • Wearable Electronic Devices*
  • Young Adult