Arrhythmia detection with transfer learning architecture integrating the developed optimization algorithm and regularization method

Abstract

Electrocardiography (ECG) is a non-invasive tool used to identify abnormalities in heart rhythm. It is used to evaluate dysfunctions in the electrical system of the heart. It offers a mechanism that does not cause any harm to patients. Being affordable makes it accessible. It provides a comprehensive assessment of the condition of the heart. Although it provides a successful analysis opportunity for arrhythmia detection, it is time-consuming and depends on the clinician’s experience. In addition, since the ECG patterns in pediatric patients are different from the ECG patterns in adults, physicians consider it a difficult and complex task. For this reason, a custom dataset of pediatric patients was created in this study. This dataset consists of 1318 abnormal beats and 1403 normal beats. MobileNetv2 transfer learning architecture was used to classify this balanced dataset. However, the stability of the results is a valuable. Therefore, the optimization algorithm that minimizes the loss function and the regularization method that controls the complexity of the model are proposed. In this direction, Proposed Optimization Algorithm V5 and Proposed Regularization Method V5 approaches have been integrated into the MobileNetv2 transfer learning model. The accuracy rates produced in the training and test datasets are 0.9801 and 0.9509, respectively. These results have acceptable improvement and stability compared to the accuracies of 0.9633 and 0.9399 produced by the original MobileNetv2 architecture on the training and test dataset, respectively. However, performance values provide limited information about the generalizability of the model. Therefore, the same processes were repeated on a more complex dataset with 6 categories. As a result of the classification, the accuracy rates for the training and test data sets were obtained as 0.9200% and 0.8975%, respectively. Training was performed under the same conditions as the training performed on 2-category datasets. Therefore, it is normal for the test dataset to experience a decrease of approximately 5%. The results obtained show that generalizations can be made for comprehensive, highly diverse and rich datasets.