Other Articles by Author(s)

Ying-Fang Lai
Hsiu-Sen Chiang

Author(s) and WSEAS

Ying-Fang Lai
Hsiu-Sen Chiang

WSEAS Transactions on Biology and Biomedicine

Print ISSN: 1109-9518
E-ISSN: 2224-2902

Volume 14, 2017

Notice: As of 2014 and for the forthcoming years, the publication frequency/periodicity of WSEAS Journals is adapted to the 'continuously updated' model. What this means is that instead of being separated into issues, new papers will be added on a continuous basis, allowing a more regular flow and shorter publication times. The papers will appear in reverse order, therefore the most recent one will be on top.

Automatic Detection of Epileptic Seizures in EEG Using Machine Learning Methods

AUTHORS: Ying-Fang Lai, Hsiu-Sen Chiang

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ABSTRACT: Epilepsy is a common neurological disorder in which the sudden onset of seizures can cause delirium, body twitching, foaming at the mouth and other symptoms requiring immediate treatment. Electroencephalogram (EEG) is the primary method for objectively detecting epilepsy in patients and must be conducted by a trained physician or specialist. Therefore, the development of methods for the rapid and accurate diagnosis of epilepsy could potentially save considerable time and cost. The This study uses Discrete Wavelet Transform to analyze physiological EEG parameters and retrieve a plurality of sub-bands. Multiple classification methods are used to compare the diverse data to select the most suitable method for classifying epilepsy EEG data and to develop a diagnostic mode. The results found that C4.5 in the sub-D2 produced diagnoses with an accuracy of up to 90%. Rule-based of decision trees can be used to quickly analyze large amounts of data, thus accelerating epilepsy diagnosis.

KEYWORDS: Electroencephalograph, Epilepsy, Discrete Wavelet, Machine Learning


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WSEAS Transactions on Biology and Biomedicine, ISSN / E-ISSN: 1109-9518 / 2224-2902, Volume 14, 2017, Art. #10, pp. 69-74

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