A Deep Learning Approach to ECG Classification for Atrial Fibrillation, Normal Sinus Rhythm, and Congestive Heart Failure.

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.