Classification of electromyogram using weight visibility algorithm with multilayer perceptron neural network

Conference proceedings article

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Publication Details

Author list: Artameeyanant P., Sultornsanee S., Chamnongthai K.

Publisher: Hindawi

Publication year: 2015

Start page: 190

End page: 194

Number of pages: 5

ISBN: 9781479960491

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925860035&doi=10.1109%2fKST.2015.7051485&partnerID=40&md5=bcb66e5f1f4eb19cb733cd7ae3d6012b

Languages: English-Great Britain (EN-GB)

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Abstract

Classifing of electromyographic (EMG) signal has been a significant issue on diagnosis for the disease since the signal is complex and non-stationary. The key on the classification is feature extraction. In this paper we propose a novel feature extraction technique based on transforming the signal to complex network via weight visibility algorithm. The feature vector is obtained from statistical mechanics of complex network. Then, multilayer perceptron neural network is employed for classification. The proposed method classified the signals into 3 cases, i.e., healthy, myopathy, and neuropathy. The experimental results show that the proposed method identified and classified the EMG signal with average accuracy of 94.75%. ฉ 2015 IEEE.

Keywords

MLPNN, Statistical Mechanics, Weight Visibility Algorithm