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Cardiac Arrhythmia, CHF, and NSR Classification With NCA-Based Feature Fusion and SVM Classifier
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Author(s): Deepak H. A. (SJB Institute of Technology, India)and Vijayakumar T. (SJB Institute of Technology, India)
Copyright: 2023
Volume: 11
Issue: 1
Pages: 24
Source title:
International Journal of Software Innovation (IJSI)
Editor(s)-in-Chief: Roger Y. Lee (Central Michigan University, USA)and Lawrence Chung (The University of Texas at Dallas, USA)
DOI: 10.4018/IJSI.315659
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Abstract
An arrhythmia is an irregular heartbeat that causes abnormal heart rhythms. Manual analysis of electrocardiogram (ECG) signals is not sufficient to quickly detect cardiac arrhythmias. This study proposes a deep learning approach based on a convolutional neural network (CNN) architecture for the classification of cardiac arrhythmias (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). First, the ECG signal is converted into a 2D image using time-frequency conversion. The scalogram is constructed using a continuous wavelet transform to extract dynamic features. With CNN, each ECG signal is broken down into heartbeats, and then each heartbeat is converted into a 2D grayscale image of the heartbeat. Morphological feature extraction was performed by segmenting the QRS complex and detecting P and T waves. A third approach to feature extraction is dual-tree complex wavelet transform (DT-CWT). In addition, all extracted features are combined using neighborhood component analysis (NCA), and features are selected to classify using a support vector machine (SVM) classifier.
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