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Proceedings Paper

Classification of ECG Arrhythmia using symbolic dynamics through fuzzy clustering neural network
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Paper Abstract

This paper presents automatic ECG arrhythmia classification method using symbolic dynamics through hybrid classifier. The proposed method consists of four steps: pre-processing, data extraction, symbolic time series construction and classification. In the proposed method, initially ECG signals are pre-processed to remove noise. Further, QRS complex is extracted followed by R peak detection. From R peak value, symbolic time series representation is formed. Finally, the symbolic time series is classified using Fuzzy clustering Neural Network (FCNN). To evaluate the proposed method we conducted the experiments on MIT-BIH dataset and compared the results with Support Vector Machine (SVM) and Radial Basis Function Neural Network (RBFNN) classifiers. The experimental results reveal that the FCNN classifier outperforms other two classifiers.

Paper Details

Date Published: 26 July 2018
PDF: 10 pages
Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 1082818 (26 July 2018); doi: 10.1117/12.2501850
Show Author Affiliations
C. K. Roopa, JSS Research Foundation (India)
B. S. Harish, Sri Jayachamarajendra College of Engineering (India)
S. V. Aruna Kumar, Sri Jayachamarajendra College of Engineering (India)


Published in SPIE Proceedings Vol. 10828:
Third International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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