An Intelligent Learning Approach for Improving ECG Signal Classification and Arrhythmia Analysis

The development of deep learning algorithms in recent years has shown promise in interpreting ECGs, as these algorithms can be trained on large datasets and can learn to identify patterns associated with different heart conditions. The advantage of these algorithms is their ability to process large amounts of data quickly and accurately, which can help improve the speed and accuracy of diagnoses, especially for patients with heart conditions. Our proposed work provides performant models based on residual neural networks to automate the diagnosis of 12-lead ECG signals with more than 25 classes comprising different cardiovascular diseases (CVDs) and a healthy sinus rhythm. We conducted an experimental study using public datasets from Germany, the USA, and China and trained two models based on Residual Neural Net-works-50 (ResNet-50) and Xception from CNN techniques, which is one of the most effective classification models. Our models achieved high performances for both training and test tasks in terms of accuracy, precision, recall, and loss, with accuracy, recall, and precision exceeding 99.87% for the two proposed models during the training and validation. The loss obtained by the end of these two phases was 3.38.10-4. With these promising results, our suggested models can serve as diagnostic aids for cardiologists to evaluate ECG signals more quickly and objectively. Further quantitative and qualitative evaluations are presented and discussed in the study, and our work can be extended to other multi-modal big biological data tied with ECG for similar sets of patients to obtain a better understanding of the proposed approach for the benefit of the medical world.