Why the study?
Deep learning approaches have received interest for medical signal processing, prompting investigation into the effectiveness of using the AlexNet architecture for reliable ECG data categorization.
A deep learning approach using the AlexNet architecture can accurately classify ECG signals into normal sinus rhythm, atrial fibrillation, and congestive heart failure with 99.21% accuracy.
Deep learning approaches for medical signal processing have received a lot of interest in recent years, especially in the field of electrocardiogram (ECG) categorization. This study investigates the effectiveness of using the AlexNet architecture, a well-known convolutional neural network (CNN) model, for reliable ECG data categorization. Our suggested model has attained an accuracy of 99.21% after extensive experimentation and validation, indicating its ability to distinguish various cardiac anomalies. The research includes the creation and refinement of a unique AlexNet-based CNN model adapted to the difficulties of ECG data. Using AlexNet's deep hierarchical features, our model outperforms in capturing complicated patterns and minor fluctuations within ECG data.
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Sadigale et al. (2023) studied this question.
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