Regularity and Matching Pursuit feature extraction for the detection of epileptic seizures.

Abstract:

BACKGROUND:The neurological disorder known as epilepsy is characterized by involuntary recurrent seizures that diminish a patient's quality of life. Automatic seizure detection can help improve a patient's interaction with her/his environment, and while many approaches have been proposed the problem is still not trivially solved. METHODS:In this work, we present a novel methodology for feature extraction on EEG signals that allows us to perform a highly accurate classification of epileptic states. Specifically, Hölderian regularity and the Matching Pursuit algorithm are used as the main feature extraction techniques, and are combined with basic statistical features to construct the final feature sets. These sets are then delivered to a Random Forests classification algorithm to differentiate between epileptic and non-epileptic readings. RESULTS:Several versions of the basic problem are tested and statistically validated producing perfect accuracy in most problems and 97.6% accuracy on the most difficult case. COMPARISON WITH EXISTING METHODS:A comparison with recent literature, using a well known database, reveals that our proposal achieves state-of-the-art performance. CONCLUSIONS:The experimental results show that epileptic states can be accurately detected by combining features extracted through regularity analysis, the Matching Pursuit algorithm and simple time-domain statistical analysis. Therefore, the proposed method should be considered as a promising approach for automatic EEG analysis.

journal_name

J Neurosci Methods

authors

Z-Flores E,Trujillo L,Sotelo A,Legrand P,Coria LN

doi

10.1016/j.jneumeth.2016.03.024

subject

Has Abstract

pub_date

2016-06-15 00:00:00

pages

107-25

eissn

0165-0270

issn

1872-678X

pii

S0165-0270(16)30030-9

journal_volume

266

pub_type

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