New Feature Extraction Approach for Epileptic EEG Signal Detection Using Time-frequency Distributions

Overview

Journal Med Biol Eng Comput

Publisher Springer

Specialties Biomedical Engineering
Medical Informatics

Date 2010 Mar 11

PMID 20217264

Citations 22

Authors

Carlos Guerrero-Mosquera

Armando Malanda Trigueros

Jorge Iriarte Franco

Angel Navia-Vazquez

Affiliations

Soon will be listed here.

Abstract

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.

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