Affiliations: [a] University Research Fellow, Periyar University, Periyar, India | [b] Assistant Professor, Periyar University, Periyar, India | [c] Faculty of Computers and Information, Benha University, Benha, Egypt
Abstract: Reliable identification of arrhythmias built by digital
signal processing of Electrocardiogram (ECG) is significant in providing
appropriate and suitable treatment to a cardiac arrhythmia patient. Due to
exploitation of ECG signals with numerous frequency noises and the
occurrence of various arrhythmic events in a cardiac rhythm, computerized
interpretation of abnormal ECG rhythms is a thought-provoking task. The
objective of this paper is to construct novel automatic classification
system for analysis of ECG signal and decision making purposes. The proposed
classification method is a hybridization of Bijective soft set and back
propagation neural network. The Hybrid Bijective soft set neural network
based classification algorithm (BISONN) is applied to classify the ECG
signals into normal and four abnormal heart beats. Initially, discrete
wavelet transform is applied before classification for signal De-noising and
feature extraction. The experimental results are obtained by evaluating the
proposed method on ECG data from the MIT-BIH arrhythmia database. The
experimental analysis of the proposed BISONN algorithm is compared with the
Multi-layered Perceptron (MLP), Decision table (DT), Naïve Bayes (NB)
and J48 classification algorithms. The performance of the classifier is
measured in terms of sensitivity, specificity, Positive predictive value,
negative predictive value, false predictive value, Matthews's correlation
coefficients, F-Measure, Folke-mallows Index and Kulcznski Index. The
acquired results clearly confirm the superiority of the BISONN algorithm as
compared with other classifiers.
