PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

New feature extraction approach for epileptic EEG signal detection using time-frequency distributions
Angel Navia-Vazquez and Carlos Guerrero-Mosquera
Medical & Biological Engineering & Computing Volume 48, Number 4, pp. 321-330, 2010. ISSN 0140-0118

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.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Brain Computer Interfaces
ID Code:6467
Deposited By:Angel Navia-Vazquez
Deposited On:17 March 2011