Journal•ISSN: 1448-5869
International Journal of Hybrid Intelligent Systems
IOS Press
About: International Journal of Hybrid Intelligent Systems is an academic journal published by IOS Press. The journal publishes majorly in the area(s): Computer science & Artificial intelligence. It has an ISSN identifier of 1448-5869. Over the lifetime, 134 publications have been published receiving 549 citations. The journal is also known as: IJHIS.
Topics: Computer science, Artificial intelligence, Ensemble learning, Artificial neural network, Genetic algorithm
Papers published on a yearly basis
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TL;DR: A novel automatic classification system for analysis of ECG signal and decision making purposes using a hybridization of Bijective soft set and back propagation neural network based algorithm, BISONN.
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 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.
19 citations