Entropy based features in FAWT framework for automated detection of epileptic seizure EEG signals

被引：0

作者：

Tanveer, M. ^{[1
]}

Pachori, R. B. ^{[2
]}

Angami, N., V ^{[1
]}

机构：

[1] Indian Inst Technol Indore, Discipline Math, Indore 453552, Madhya Pradesh, India

[2] Indian Inst Technol Indore, Discipline Elect Engn, Indore 453552, Madhya Pradesh, India

来源：

2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI) | 2018年

关键词：

Electroencephalogram (EEG); Seizure and non-seizure; Flexible analytic wavelet transform (FAWT); Robust energy-based least squares twin support vector machines (RELS-TSVM); SUPPORT VECTOR MACHINES; ANALYTIC WAVELET TRANSFORM; CLASSIFICATION; DIAGNOSIS; PATTERN; EXTRACTION; ROBUST; MODEL;

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：

Flexible analytic wavelet transform (FAWT) is suitable for the study of oscillatory signals like electroencephalogram (EEG) signals with versatile features such as shift in-variance, tunable oscillatory properties and flexible time-frequency domain. In this paper, we propose an automated method for the classification of seizure and non-seizure EEG signals using FAWT and entropy-based features such as Stein's unbiased risk estimator (SURE) entropy, log energy entropy, and Shannon entropy. The obtained features are given as input to robust energy-based least squares twin support vector machines (RELS-TSVM) for classification. The proposed method has been implemented on publicly available epilepsy database (Bonn University EEG database) and is comparable with the existing methods with a maximum accuracy of 100% for the classification of seizure and non-seizure EEG signals.

引用

页码：1946 / 1952

页数：7

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