title: Optimal features for online seizure detection.
abstract: This study identifies characteristic features in scalp EEG that simultaneously give the best discrimination between epileptic seizures and background EEG in minimally pre-processed scalp data; and have minimal computational complexity to be suitable for online, real-time analysis. The discriminative performance of 65 previously reported features has been evaluated in terms of sensitivity, specificity, area under the sensitivity-specificity curve (AUC), and relative computational complexity, on 47 seizures (split in 2,698 2 s sections) in over 172 h of scalp EEG from 24 adults. The best performing features are line length and relative power in the 12.5-25 Hz band. Relative power has a better seizure detection performance (AUC = 0.83; line length AUC = 0.77), but is calculated after the discrete wavelet transform and is thus more computationally complex. Hence, relative power achieves the best performance for offline detection, whilst line length would be preferable for online low complexity detection. These results, from the largest systematic study of seizure detection features, aid future researchers in selecting an optimal set of features when designing algorithms for both standard offline detection and new online low computational complexity detectors.
authors.affiliation: Electrical and Electronic Engineering Department, Imperial College London, London, UK. lojini.logesparan04@imperial.ac.uk
authors.forename: Lojini
authors.initials: L
authors.lastname: Logesparan
authors.forename: Alexander J
authors.initials: AJ
authors.lastname: Casson
authors.forename: Esther
authors.initials: E
authors.lastname: Rodriguez-Villegas
mesh_terms: D000465:Algorithms; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted