Comparison of Time-Frequency Feature Extraction Methods for EEG Signals Classification

被引：0

作者：

Rutkowski, Grzegorz ^{[1
]}

Patan, Krzysztof ^{[1
]}

Lesniak, Pawel ^{[2
]}

机构：

[1] Univ Zielona Gora, Inst Control & Computat Engn, Ul Podgorna 50, PL-65246 Zielona Gora, Poland

[2] Prov Hosp Zielona Gora, Ward Neurol & Strokes, Zielona Gora, Poland

来源：

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, PT II | 2013年 / 7895卷

关键词：

EEG signals; time-frequency representation; classification; SEIZURE DETECTION; NEURAL-NETWORK; ELECTROENCEPHALOGRAM; TRANSFORM;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Biomedical signal analysis can assist the diagnostic process for each neurological dysfunction. At that time the diagnostic tool continues to evolve, which is related to the development of both technological and innovative solutions proposed by scientists. One of possible solutions for automatic EEG analysis is to use modern signal processing tools, which are able to give a time-frequency representation of a signal. This paper presents a comparison of methods such as the discrete wavelet transform, the matching pursuit algorithm, and the S transform for feature extraction and then classification of neurological disorders. The research was carried out using real EEG recordings of epileptic patients as well as healthy subjects.

引用

页码：320 / +

页数：2

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