New feature extraction approach for epileptic EEG signal detection using time-frequency distributions

Carlos Guerrero-Mosquera, Armando Malanda Trigueros, Jorge Iriarte Franco, Ángel Navia-Vázquez

Producció científica: Article en revista indexadaArticleAvaluat per experts

76 Cites (Scopus)

Resum

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.

Idioma originalAnglès
Pàgines (de-a)321-330
Nombre de pàgines10
RevistaMedical and Biological Engineering and Computing
Volum48
Número4
DOIs
Estat de la publicacióPublicada - d’abr. 2010
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