He Youfu, Zhou Yu, Qian Yu, Liu Jingjie, Zhang Jinyan, Liu Debin, Wu Qiang
Medical College, Guizhou University, Guiyang, Guizhou, China.
Department of Cardiology, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
Front Cardiovasc Med. 2025 Jan 6;11:1473482. doi: 10.3389/fcvm.2024.1473482. eCollection 2024.
The risk of mortality associated with cardiac arrhythmias is considerable, and their diagnosis presents significant challenges, often resulting in misdiagnosis. This situation highlights the necessity for an automated, efficient, and real-time detection method aimed at enhancing diagnostic accuracy and improving patient outcomes.
The present study is centered on the development of a portable deep learning model for the detection of arrhythmias via electrocardiogram (ECG) signals, referred to as CardioAttentionNet (CANet). CANet integrates Bi-directional Long Short-Term Memory (BiLSTM) networks, Multi-head Attention mechanisms, and Depthwise Separable Convolution, thereby facilitating its application in portable devices for early diagnosis. The architecture of CANet allows for effective processing of extended ECG patterns and detailed feature extraction without a substantial increase in model size.
Empirical results indicate that CANet outperformed traditional models in terms of predictive performance and stability, as confirmed by comprehensive cross-validation. The model demonstrated exceptional capabilities in detecting cardiac arrhythmias, surpassing existing models in both cross-validation and external testing scenarios. Specifically, CANet achieved high accuracy in classifying various arrhythmic events, with the following accuracies reported for different categories: Normal (97.37 ± 0.30%), Supraventricular (98.09 ± 0.25%), Ventricular (92.92 ± 0.09%), Atrial Fibrillation (99.07 ± 0.13%), and Unclassified arrhythmias (99.68 ± 0.06%). In external evaluations, CANet attained an average accuracy of 94.41%, with the area under the curve (AUC) for each category exceeding 99%, thereby demonstrating its substantial clinical applicability and significant advancements over traditional models.
The deep learning model proposed in this study has the potential to enhance the accuracy of early diagnosis for various types of arrhythmias. Looking ahead, this technology is anticipated to provide improved medical services for patients with heart disease through continuous, non-invasive monitoring and timely intervention.
与心律失常相关的死亡风险相当高，其诊断面临重大挑战，常常导致误诊。这种情况凸显了需要一种自动化、高效且实时的检测方法，以提高诊断准确性并改善患者预后。
本研究聚焦于开发一种通过心电图（ECG）信号检测心律失常的便携式深度学习模型，称为心脏注意力网络（CANet）。CANet集成了双向长短期记忆（BiLSTM）网络、多头注意力机制和深度可分离卷积，从而便于其在便携式设备中用于早期诊断。CANet的架构允许在不显著增加模型大小的情况下有效处理扩展的心电图模式并进行详细特征提取。
实证结果表明，通过全面交叉验证证实，CANet在预测性能和稳定性方面优于传统模型。该模型在检测心律失常方面展现出卓越能力，在交叉验证和外部测试场景中均超越现有模型。具体而言，CANet在对各种心律失常事件进行分类时实现了高精度，不同类别的准确率如下：正常（97.37±0.30%）、室上性（98.09±0.25%）、室性（92.92±0.09%）、心房颤动（99.07±0.13%）和未分类心律失常（99.68±0.06%）。在外部评估中，CANet的平均准确率达到94.41%，每个类别的曲线下面积（AUC）超过99%，从而证明了其显著的临床适用性以及相对于传统模型的重大进步。
本研究中提出的深度学习模型有潜力提高各类心律失常的早期诊断准确性。展望未来，预计该技术将通过持续的无创监测和及时干预为心脏病患者提供更好的医疗服务。
