New feature extraction approach for epileptic EEG signal detection using time-frequency distributions.
This paper describes a new method to identify seizures in electroencephalogram (EEG) signals using feature extraction in time-frequency distributions (TFDs). Particularly, the method extracts features from the Smoothed Pseudo Wigner-Ville distribution using tracks estimated from the McAulay-Quatieri sinusoidal model. The proposed features are the length, frequency, and energy of the principal track. We evaluate the proposed scheme using several datasets and we compute sensitivity, specificity, F-score, receiver operating characteristics (ROC) curve, and percentile bootstrap confidence to conclude that the proposed scheme generalizes well and is a suitable approach for automatic seizure detection at a moderate cost, also opening the possibility of formulating new criteria to detect, classify or analyze abnormal EEGs.
Guerrero-Mosquera
Carlos
C
Signal Processing and Communications Department, University Carlos III of Madrid, Madrid, Spain. cguerrero@tsc.uc3m.es
Trigueros
Armando Malanda
AM
Franco
Jorge Iriarte
JI
Navia-Vázquez
Angel
A
D000328:Adult; D016477:Artifacts; D004569:Electroencephalography / Q000379:methods; D004828:Epilepsies, Partial / Q000175:diagnosis* / Q000503:physiopathology; D006801:Humans; D008959:Models, Neurological; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted