New feature extraction approach for epileptic EEG signal detection using time-frequency distributions. - Guerrero-Mosquera C; Malanda Trigueros A; Iriarte Franco J; Navia-Vázquez A; Guerrero-Mosquera, Carlos; Trigueros, Armando Malanda; Franco, Jorge Iriarte; Navia-Vázquez, Angel (Medical & Biological Engineering & Computing, 2010)
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.