Entropy | |
An Integrated Index for the Identification of Focal Electroencephalogram Signals Using Discrete Wavelet Transform and Entropy Measures | |
Rajeev Sharma1  Ram Bilas Pachori1  U. Rajendra Acharya2  | |
[1] Discipline of Electrical Engineering, Indian Institute of Technology Indore, Indore 452017, India; E-Mail:;Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore; E-Mail: | |
关键词: EEG; epilepsy; wavelet; entropy; classifier; | |
DOI : 10.3390/e17085218 | |
来源: mdpi | |
【 摘 要 】
The dynamics of brain area influenced by focal epilepsy can be studied using focal and non-focal electroencephalogram (EEG) signals. This paper presents a new method to detect focal and non-focal EEG signals based on an integrated index, termed the focal and non-focal index (FNFI), developed using discrete wavelet transform (DWT) and entropy features. The DWT decomposes the EEG signals up to six levels, and various entropy measures are computed from approximate and detail coefficients of sub-band signals. The computed entropy measures are average wavelet, permutation, fuzzy and phase entropies. The proposed FNFI developed using permutation, fuzzy and Shannon wavelet entropies is able to clearly discriminate focal and non-focal EEG signals using a single number. Furthermore, these entropy measures are ranked using different techniques, namely the Bhattacharyya space algorithm, Student’s
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
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RO202003190009142ZK.pdf | 1131KB | download |