Volume 14, Issue 2 (9-2017)                   JSDP 2017, 14(2): 25-42 | Back to browse issues page

DOI: 10.18869/acadpub.jsdp.14.2.25

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Ghasemi J, Kord S, Gholami M. classification of Cardiac Arrhythmias based on combination of the results of Neural Networks using Dempster-Shefer Evidence Theory. JSDP. 2017; 14 (2) :25-42
URL: http://jsdp.rcisp.ac.ir/article-1-468-en.html

​ Assistant Professor ​ University of Mazandaran
Abstract:   (265 Views)

Cardiac arrhythmias are one of the most common heart diseases that may cause the death of the patient. Therefore, it is extremely important to detect cardiac arrhythmias.  3 categories of arrhythmia, namely, PAC, PVC, and normal are considered in this paper based on classifier fusion using evidence theory. At first R peaks of ECG were identified. Then, the line features including ECG RMSSD, SDNN and HR Mean, and also non-linear characteristics were obtained by using SVD. The combination of these features results is given in MLP, Cascade Feed Forward and RBF neural networks. Next the principle of uncertainty about their response was checked, and finally, the results of these classifiers were combined by applying evidence theory. ECG processing is not needed to remove noise, however, the proposed method, in the presence of noise, is able to detect the cardiac arrhythmia, in best situation with 98% sensitivity.

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Type of Study: Research | Subject: Paper
Received: 2015/12/5 | Accepted: 2016/10/29 | Published: 2017/10/21 | ePublished: 2017/10/21

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