DEEP LEARNING FRAMEWORK WITH OPTIMIZATIONS FOR AUTOMATIC DETECTION OF ARRHYTHMIA FROM ECG DATA

The WHO states cardiovascular disorders are a significant health concern, emphasizing the need for technical advancements to provide diagnostic instruments that can identify arrhythmias or irregular heartbeats in electrocardiograms. As AI has grown in popularity, especially DL methods that have shown promise in analyzing medical data, it is imperative to apply these learning-based strategies to improve arrhythmia detection and classification performance. CD diagnosis is a promising use of current DL models, such as CNNs. Nevertheless, these models must be improved to diagnose diseases as effectively as possible. This study suggests a DL-based system for automatically identifying and categorizing electrocardiogram arrhythmias. To further apply this framework and efficiently identify arrhythmias, we provide an approach called LbADC. Our empirical investigation, which used the PhysioNet 2017 Challenge dataset as a benchmark, showed that the suggested DL architecture successfully identifies and categorizes arrhythmias in ECG data. According to the experimental data, the tested CNN model outperformed several current DL models, including LeNet, ResNet50, and U-Net, with a maximum specificity of 96.04%. Therefore, to develop a clinical decision support system for the automated screening of CD disorders, the suggested framework, the improved CNN model, and the underlying algorithm may be included in any current healthcare application.