Key result
Hybrid deep learning architecture combines 3DCNN-ResNet and ASDSO optimization to classify five ECG arrhythmia classes.
Why the study?
Existing algorithms for automatic ECG arrhythmia classification have low stratification rates due to unreliable signal features or limited classifier generalization, leaving automated diagnosis challenging.
Does a hybrid 3DCNN-ResNet model optimized with ASDSO improve the classification of arrhythmias from ECG signals?
Does a hybrid 3DCNN-ResNet model optimized with ASDSO improve the classification of arrhythmias from ECG signals?
A novel hybrid deep learning technique using 3DCNN-ResNet and ASDSO optimization demonstrates potential for automated classification of cardiac arrhythmias from ECG signals.
Hybrid ECG model shows promise for arrhythmia classification; leaves open prospective clinical validation before adoption.
Arrhythmia is characterized by aberrant electrical activity of the heart, which can be identified the changes in the Electrocardiogram (ECG).Automatic ECG detection is needed because it is mainly used for detecting arrhythmias.Although several algorithms are implemented for the automatic classification of cardiac arrhythmias based on the characteristics of the ECG, their stratification rate is very less because of the unreliable features of signal characteristics or limited generalization capability of the classifier and it is still difficult to diagnose the arrhythmia disease automatically.At this work, they propose a new hybrid deep learning technique for the classification of arrhythmia from the ECG signal.Initially, the wanted ECG signal is collected from the standard websites and then it is assigned to the preprocessing technique.The preprocessing techniques includes the artifacts removal and peak detection techniques noise removal for the elimination of the unwanted distortions and the noise present in the signal then the resultant signal is fed to the Short-time Fourier transform (STFT) to achieve the spectrogram signals and then the spectrogram signal.Thus, the resultant spectrogram signal is given to the hybrid deep learning architecture that includes the 3DCNN-ResNet for diagnosing the arrhythmia disease.Here, the parameter optimizations take place using the hybrid Artificial Showering Dolphin Swarm Optimization (ASDSO) to increase the classification performance.It classifies the signal into five prominent classes that is Premature Ventricular Contraction (V), Right Bundle Branch Block (RBBB or R), Normal Sinus Rhythm (N), Left Bundle Branch Block (LBBB or L), and Atrial Premature Beat (A).The success of the proposed model is validated through diverse benchmark datasets with the performance validation like recall, precision, accuracy, f-measure and some negative measures.
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Munawar et al. (2023) studied Arrhythmia. Hybrid deep learning technique (3DCNN-ResNet with ASDSO optimization) was evaluated on Classification performance (recall, precision, accuracy, f-measure). A hybrid deep learning architecture combining 3DCNN-ResNet and ASDSO optimization was developed to classify ECG signals into five arrhythmia classes.
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