Hybrid Bijective soft set - Neural network for ECG arrhythmia classification

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 be- fore 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), Naive 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.