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|Title:||Classification of electromyogram using vertical visibility algorithm with support vector machine||Authors:||Artameeyanant, P.
|Keywords:||Community Structure;Complex Network;EMG Signal;Vertical Visibility Algorithm||Issue Date:||2014||Publisher:||Scopus
University of the Thai Chamber of Commerce
|Source:||P. Artameeyanant, S. Sultornsanee, K. Chamnongthai, K. Higuchi (2014) Classification of electromyogram using vertical visibility algorithm with support vector machine. AsiaPacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014.||Conference:||AsiaPacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2014||Abstract:||Analyzing the electromyogram is an important issue on diagnosis of neuromuscular diseases. The classification of electromyogram signal plays a significant role in this issue. Since the characteristic of the signals is complex and nonstationary, so the complex network is an appropriate tool in extracting feature of the signal. In this paper we propose a novel feature extraction technique based on transforming the signal to complex network via vertical visibility algorithm. Characteristic on the measurements of community structure and distance property are examined. The pattern on the relationship of nodes in the network is investigated. Support vector machine was employed for classification. The proposed method can classify the signals into 3 cases, i.e., healthy, myopathy, and neuropathy, with remarkable experimental results.||URI:||https://scholar.utcc.ac.th/handle/6626976254/3451||Rights:||This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.|
|Appears in Collections:||RSO: Conference Papers|
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