Key result
A deep learning approach combining CNN, NCA-based feature fusion, and an SVM classifier was developed for the classification of cardiac arrhythmias, congestive heart failure, and normal sinus rhythm.
Why the study?
Manual analysis of ECG signals is not sufficient to quickly detect cardiac arrhythmias.
A proposed deep learning and feature fusion approach aims to automate and improve the classification of ECG signals for arrhythmias and congestive heart failure.
May aid rapid automated arrhythmia detection; leaves open prospective clinical validation before practice adoption.
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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Deepak et al. (2022) studied Cardiac arrhythmias, congestive heart failure, and normal sinus rhythm. CNN architecture with NCA-based feature fusion and SVM classifier was evaluated on Classification of cardiac arrhythmias, congestive heart failure, and normal sinus rhythm. A deep learning approach combining CNN, NCA-based feature fusion, and an SVM classifier was developed for the classification of cardiac arrhythmias, congestive heart failure, and normal sinus rhythm.
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