Automatic multimodal detection for long-term seizure documentation in epilepsy

Clin Neurophysiol. 2017 Aug;128(8):1466-1472. doi: 10.1016/j.clinph.2017.05.013. Epub 2017 May 25.


Objective: This study investigated sensitivity and false detection rate of a multimodal automatic seizure detection algorithm and the applicability to reduced electrode montages for long-term seizure documentation in epilepsy patients.

Methods: An automatic seizure detection algorithm based on EEG, EMG, and ECG signals was developed. EEG/ECG recordings of 92 patients from two epilepsy monitoring units including 494 seizures were used to assess detection performance. EMG data were extracted by bandpass filtering of EEG signals. Sensitivity and false detection rate were evaluated for each signal modality and for reduced electrode montages.

Results: All focal seizures evolving to bilateral tonic-clonic (BTCS, n=50) and 89% of focal seizures (FS, n=139) were detected. Average sensitivity in temporal lobe epilepsy (TLE) patients was 94% and 74% in extratemporal lobe epilepsy (XTLE) patients. Overall detection sensitivity was 86%. Average false detection rate was 12.8 false detections in 24h (FD/24h) for TLE and 22 FD/24h in XTLE patients. Utilization of 8 frontal and temporal electrodes reduced average sensitivity from 86% to 81%.

Conclusion: Our automatic multimodal seizure detection algorithm shows high sensitivity with full and reduced electrode montages.

Significance: Evaluation of different signal modalities and electrode montages paces the way for semi-automatic seizure documentation systems.

Keywords: Algorithm; Automatic; ECG; EEG; EMG; Multimodal; Seizure detection.

Publication types

  • Multicenter Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Electrocardiography / methods*
  • Electroencephalography / methods*
  • Electromyography / methods*
  • Epilepsy / diagnosis*
  • Epilepsy / physiopathology*
  • Humans
  • Retrospective Studies
  • Seizures / diagnosis
  • Seizures / physiopathology
  • Time Factors