Article: Classification of heart rhythm disorders using instructive features and artificial neural networks Journal: International Journal of Medical Engineering and Informatics (IJMEI) 2018 Vol.10 No.4 pp.359 - 381 Abstract: Accurate detection of the heart rhythm disorders at an early stage is helpful for improving survival rate. This paper presents an automated detection and classification methods of cardiac arrhythmia by time-frequency analysis of the recorded ECG signals from MIT-BIH database. The discrete wavelet transform has been used to eliminate noises in order to enhance the quality of signals and adaptive thresholding-based Hilbert transform has been used to find precise R-peaks. Temporal, morphological and statistical features were extracted from each heartbeat and has been used as input to the classifier to detect five cardiac arrhythmia beats. The results show less detection error rate of 0.17% in detecting QRS complex. The MLP-BP, RBF-NN, and the PNN classifiers provide an average accuracy of 98.72%, 99.77% and 99.16% respectively. The result indicates the efficiency of the proposed method in classifying ECG beats which is useful in diagnosis of cardiac arrhythmias. Inderscience Publishers - linking academia, business and industry through research

Title: Classification of heart rhythm disorders using instructive features and artificial neural networks

Authors: Santanu Sahoo; Priti Das; Prativa Biswal; Sukanta Sabut

Addresses: Department of Electronics and Communication Engineering, Institute of Technical Education and Research, SOA University, Odisha, India ' Department of Phamacology, SCB Medical College and Hospital, Cuttack, Odisha, India ' Department of Electronics and Communication Engineering, Institute of Technical Education and Research, SOA University, Odisha, India ' Department of Electronics Engineering, Ramrao Adik Institute of Technology, DY Patil Vidyapeetha, Navi Mumbai, India

Abstract: Accurate detection of the heart rhythm disorders at an early stage is helpful for improving survival rate. This paper presents an automated detection and classification methods of cardiac arrhythmia by time-frequency analysis of the recorded ECG signals from MIT-BIH database. The discrete wavelet transform has been used to eliminate noises in order to enhance the quality of signals and adaptive thresholding-based Hilbert transform has been used to find precise R-peaks. Temporal, morphological and statistical features were extracted from each heartbeat and has been used as input to the classifier to detect five cardiac arrhythmia beats. The results show less detection error rate of 0.17% in detecting QRS complex. The MLP-BP, RBF-NN, and the PNN classifiers provide an average accuracy of 98.72%, 99.77% and 99.16% respectively. The result indicates the efficiency of the proposed method in classifying ECG beats which is useful in diagnosis of cardiac arrhythmias.

Keywords: heart rhythm; discrete wavelet transform; DWT; Hilbert transform; adaptive thresholding; QRS complex; NN classifier.

DOI: 10.1504/IJMEI.2018.095085

International Journal of Medical Engineering and Informatics, 2018 Vol.10 No.4, pp.359 - 381

Received: 10 May 2017
Accepted: 19 May 2017
Published online: 01 Oct 2018 *

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Title: Classification of heart rhythm disorders using instructive features and artificial neural networks

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