MCA Based Epilepsy EEG Classification Using Time Frequency Domain Features

被引：0

作者：

Mahapatra, Arindam Gajendra

Singh, Balbir ^{[2
]}

Horio, Keiichi ^{[1
]}

Wagatsuma, Hiroaki

机构：

[1] Kyushu Inst Technol, Kitakyushu, Fukuoka, Japan

[2] Natl Inst Physiol Sci, Okazaki, Aichi, Japan

来源：

2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) | 2018年

关键词：

SEIZURE DETECTION; SIGNALS; DECOMPOSITION; PREDICTION; RECORDS; IMAGE;

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：

In this work, we proposed a morphological component analysis (MCA) based method for epilepsy classification using the explicit dictionary of independent redundant transforms to decomposes the electroencephalogram (EEG) by considering it's morphology. Output components of MCA are represented into analytical form by using Hilbert transform. Then features, parameter's ratio of bandwidth square, mean square frequency and fractional contributions to dominant frequency were extracted to discriminate epilepsy EEG by support vector machine (SVM). These features have shown classification results comparable to previous works.

引用

页码：3398 / 3401

页数：4

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