Elsheikhy Donia H, Hassan Abdelwahab S, Yhiea Nashwa M, Fareed Ahmed M, Rashed Essam A
Department of Mathematics, Faculty of Science, Suez Canal University, Ismailia 41522, Egypt.
Faculty of Informatics and Computer Science, The British University in Egypt (BUE), Cairo 11837, Egypt.
Sensors (Basel). 2025 Sep 5;25(17):5542. doi: 10.3390/s25175542.
Cardiovascular diseases are known as major contributors to death globally. Accurate identification and classification of cardiac arrhythmias from electrocardiogram (ECG) signals is essential for early diagnosis and treatment of cardiovascular diseases. This research introduces an innovative deep learning architecture that integrates Convolutional Neural Networks with a channel attention mechanism, enhancing the model's capacity to concentrate on essential aspects of the ECG signals. Unlike most prior studies that depend on single-lead data or complex hybrid models, this work presents a novel yet simple deep learning architecture to classify five arrhythmia classes that effectively utilizes both 2-lead and 12-lead ECG signals, providing more accurate representations of clinical scenarios. The model's performance was evaluated on the MIT-BIH and INCART arrhythmia datasets, achieving accuracies of 99.18% and 99.48%, respectively, along with F1 scores of 99.18% and 99.48%. These high-performance metrics demonstrate the model's ability to differentiate between normal and arrhythmic signals, as well as accurately identify various arrhythmia types. The proposed architecture ensures high accuracy without excessive complexity, making it well-suited for real-time and clinical applications. This approach could improve the efficiency of healthcare systems and contribute to better patient outcomes.
心血管疾病是全球已知的主要死因。从心电图（ECG）信号中准确识别和分类心律失常对于心血管疾病的早期诊断和治疗至关重要。本研究引入了一种创新的深度学习架构，该架构将卷积神经网络与通道注意力机制相结合，增强了模型专注于ECG信号关键方面的能力。与大多数依赖单导联数据或复杂混合模型的先前研究不同，这项工作提出了一种新颖且简单的深度学习架构，用于对五种心律失常类别进行分类，该架构有效利用了双导联和12导联ECG信号，更准确地呈现临床情况。该模型在麻省理工学院 - 贝斯以色列女执事医疗中心（MIT - BIH）和INCART心律失常数据集上进行了评估，准确率分别达到99.18%和99.48%，F1分数分别为99.18%和99.48%。这些高性能指标证明了该模型区分正常和心律失常信号的能力，以及准确识别各种心律失常类型的能力。所提出的架构确保了高精度且不过度复杂，使其非常适合实时和临床应用。这种方法可以提高医疗系统的效率，并有助于改善患者的治疗效果。
