A study of Morphology-Based Wavelet Features and Multiple-Wavelet strategy for EEG Signal Classification: Results and Selected Statistical Analysis

被引：0

作者：

Zhou, Jing ^{[1
]}

Schalkoff, Robert J. ^{[1
]}

Dean, Brian C.

Halford, Jonathan J.

机构：

[1] Clemson Univ, Dept Elect & Comp Engn, Clemson, SC 29631 USA

来源：

2013 35TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2013年

关键词：

SYSTEM;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Automatic detection and classification of Epileptiform transients is an open and important clinical issue. In this paper, we test 5 feature sets derived from a group of morphology-based wavelet features and compare the results with that of a Guler-suggested feature set. We also implement a multiple-mother-wavelet strategy and compare performance with the usual single-mother-wavelet strategy. The results indicate that both the derived features and the multiple-mother-wavelet strategy improved classifier performance, using a variety of performance measures. We assess the statistical significance of the performance improvement of the new feature sets/strategy. In most cases, the performance improvement is either significant or highly significant.

引用

页码：5998 / 6002

页数：5

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