Citation

Deivasigamani, Subbramania Pattar and Senthilpari, Chinnaiyan and Wong, Hin Yong (2021) Machine learning method based detection and diagnosis for epilepsy in EEG signal. Journal of Ambient Intelligence and Humanized Computing (2020), 12. pp. 4215-4221. ISSN 1868-5137, 1868-5145

Text 10.1007@s12652-020-01816-3.pdf - Published Version Restricted to Repository staff only Download (784kB)

Abstract

The epileptic seizure can be detected using electroencephalogram (EEG) signals. The detection of epileptogenic region in brain is important for the detection of epilepsy disease. The signals from epileptogenic region in brain are focal signal and the signal from normal regions in brain is non-focal signal. Hence, the detection of focal signal is important for epilepsy disease detection. This paper proposes an automatic detection and diagnosis of EEG signals for epilepsy disease using soft computing approaches as adaptive neuro fuzzy inference system (ANFIS) and neural networks (NN). In this paper, the features from decomposed coefficients as bias (B), weight feature (W), entropy(E), activity feature (AF), mobility feature (MF), complexity feature (CF), skewness (S) and kurtosis (K) are extracted for the classification of EEG signals into either focal or non-focal signals for epilepsy disease detection and diagnosis. The detection of focal signal is achieved by ANFIS classifier and the diagnosis of the severity levels in focal signal is achieved by NN classification approach. The proposed method is used in many clinical diagnosis.

Item Type:	Article
Uncontrolled Keywords:	Machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	22 Dec 2020 06:34
Last Modified:	30 Jun 2021 15:53
URII:	http://shdl.mmu.edu.my/id/eprint/7983

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