Neighborhood rough set based ECG signal classification for diagnosis of cardiac diseases

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Cardiac diseases are one of the foremost reasons of mortality. Hence, the early detection of cardiac diseases based on electrocardiogram (ECG) is important for delivering appropriate and timely treatment to the heart patients and it is increasing the heart patient’s survival. Recent trends in clinical decision making systems appeal automation in ECG signal processing and beat classification. Automatic beat classification is a significant method to support clinical specialists to categorize arrhythmia signals in ECG recording. The main objective of this paper is to construct novel automatic classification system for analysis of ECG signal and decision making purposes. The proposed method involves three main parts: De-noising, feature extraction and classification. Initially, discrete wavelet transform (DWT) is applied before classification for signal De-noising and feature extraction. In this work, neighborhood rough set is applied to classify the ECG signals into normal and four abnormal heart beats. The presence of neighborhood rough set classification algorithm (NRSC) produces very exciting recognition and classification abilities through a wide range of biomedical signal processing. The experimental analysis of the proposed NRSC algorithm is compared with the multi-layered perceptron, decision table, Naive Bayes and J48 classification algorithms. Here, the performance of classification algorithms has been evaluated 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 showed that the proposed algorithm attained 99.32 % of the classification accuracy using NRSC and DWT. Results indicated that the performance of this proposed NRSC classification method was remarkably superior to that of other classification techniques.