Akalın Fatma, Çavdaroğlu Pınar Dervişoğlu, Orhan Mehmet Fatih
Department of Information Systems Engineering, Faculty of Computer and Information Sciences, Sakarya University, Sakarya, Turkey.
Department of Pediatrics, Division of Pediatric Cardiology, Faculty of Medicine, Sakarya University, Sakarya, Turkey.
BMC Biomed Eng. 2025 Jul 1;7(1):8. doi: 10.1186/s42490-025-00094-4.
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.
心电图（ECG）是一种用于识别心律异常的非侵入性工具。它用于评估心脏电系统的功能障碍。它提供了一种对患者无任何伤害的机制。价格实惠使其易于获得。它能对心脏状况进行全面评估。尽管它为心律失常检测提供了成功的分析机会，但它耗时且依赖临床医生的经验。此外，由于儿科患者的心电图模式与成人不同，医生认为这是一项困难且复杂的任务。因此，本研究创建了一个儿科患者的定制数据集。该数据集由1318个异常搏动和1403个正常搏动组成。使用MobileNetv2迁移学习架构对这个平衡数据集进行分类。然而，结果的稳定性很重要。因此，提出了最小化损失函数的优化算法和控制模型复杂度的正则化方法。在此方向上，将提出的优化算法V5和提出的正则化方法V5集成到MobileNetv2迁移学习模型中。训练数据集和测试数据集产生的准确率分别为0.9801和0.9509。与原始MobileNetv2架构在训练数据集和测试数据集上分别产生的0.9633和0.9399的准确率相比，这些结果有可接受的提升和稳定性。然而，性能值关于模型的泛化能力提供的信息有限。因此，在一个有6类的更复杂数据集上重复相同的过程。分类结果是，训练数据集和测试数据集的准确率分别为0.9200%和0.8975%。训练是在与2类数据集上进行的训练相同的条件下进行的。因此，测试数据集准确率下降约5%是正常的。所得结果表明，可以对全面、高度多样且丰富的数据集进行泛化。
