Wu Xidong, Yan Mingke, Wang Renqiao, Xie Liping
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, PR China.
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, 110169, PR China.
Comput Methods Programs Biomed. 2025 Apr;261:108606. doi: 10.1016/j.cmpb.2025.108606. Epub 2025 Jan 20.
Atrial fibrillation (AF) is a significant cause of life-threatening heart disease due to its potential to lead to stroke and heart failure. Although deep learning-assisted diagnosis of AF based on ECG holds significance in clinical settings, it remains unsatisfactory due to insufficient consideration of noise and redundant features. In this work, we propose a novel multiscale feature-enhanced gating network (MFEG Net) for AF diagnosis.
The network integrates multiscale convolution, adaptive feature enhancement (FE), and dynamic temporal processing. The multiscale convolution helps capture global and local information. The FE module consists of a soft-threshold residual shrinkage component, a dilated convolution module, and a Squeeze-and-Excitation (SE) module, eliminating redundant features and emphasizing effective features. The design allows the network to focus on the most relevant AF features, thereby enhancing its robustness and accuracy in the presence of noise and irrelevant information. The dynamic temporal module helps the network learn and recognize the time dependence associated with AF. The novel design endows the model with excellent robustness to cope with random noise in real-world environments.
Compared with the state-of-the-art methods, our model exhibits excellent classification performance with an accuracy of 0.930, an F1 score of 0.883, and remarkable resilience to noise interference on the PhysioNet Challenge 2017 dataset. Moreover, the model was trained on the CinC2017 database and validated on the CPSC2018 database and AFDB database, achieving accuracies of 0.908 and 0.938, respectively.
The excellent classification performance of MFEG Net, coupled with its robustness in processing noisy electrocardiogram signals, makes it a powerful method for automatic atrial fibrillation detection. This method has made significant progress over state-of-the-art methods and may alleviate the burden of manual diagnosis for clinical doctors.
心房颤动（AF）是一种危及生命的心脏病的重要病因，因其有可能导致中风和心力衰竭。尽管基于心电图的深度学习辅助房颤诊断在临床环境中具有重要意义，但由于对噪声和冗余特征考虑不足，其效果仍不尽人意。在这项工作中，我们提出了一种用于房颤诊断的新型多尺度特征增强门控网络（MFEG Net）。
该网络集成了多尺度卷积、自适应特征增强（FE）和动态时间处理。多尺度卷积有助于捕获全局和局部信息。FE模块由软阈值残差收缩组件、扩张卷积模块和挤压激励（SE）模块组成，可消除冗余特征并强调有效特征。这种设计使网络能够专注于最相关的房颤特征，从而在存在噪声和无关信息的情况下提高其鲁棒性和准确性。动态时间模块有助于网络学习和识别与房颤相关的时间依赖性。这种新颖的设计赋予模型出色的鲁棒性，以应对现实世界环境中的随机噪声。
与现有最先进的方法相比，我们的模型在PhysioNet Challenge 2017数据集上表现出出色的分类性能，准确率为0.930，F1分数为0.883，并且对噪声干扰具有显著的抵抗力。此外，该模型在CinC2017数据库上进行训练，并在CPSC2018数据库和AFDB数据库上进行验证，分别达到了0.908和0.938的准确率。
MFEG Net出色的分类性能，以及其在处理嘈杂心电图信号时的鲁棒性，使其成为自动检测心房颤动的有力方法。该方法相对于现有最先进的方法取得了显著进展，可能会减轻临床医生的手动诊断负担。
