Center for Biomedical Engineering, School of Information Science and Technology, Fudan University, Shanghai, 200433, PR China.
Department of Cardiology, Xinghua City People's Hospital, Jiangsu, 225700, PR China.
Comput Biol Med. 2022 Sep;148:105863. doi: 10.1016/j.compbiomed.2022.105863. Epub 2022 Jul 15.
The reliable detection of atrial fibrillation (AF) is of great significance for monitoring disease progression and developing tailored care paths. In this work, we proposed a novel and robust method based on deep learning for the accurate detection of AF. Using RR interval sequences, a multiscale grouped convolutional neural network (MGNN) combined with self-attention was designed for automatic feature extraction, and AF and non-AF classification. An average accuracy of 97.07% was obtained in the 5-fold cross-validation. The generalization ability of the proposed MGNN was further independently tested on four other unseen datasets, and the accuracy was 92.23%, 96.86%, 94.23% and 95.91%. Moreover, comparison of the network structures indicated that the MGNN had not only better detection performance but also lower computational complexity. In conclusion, the proposed model is shown to be an efficient AF detector that has great potential for use in clinical auxiliary diagnosis and long-term home monitoring based on wearable devices.
可靠地检测心房颤动（AF）对于监测疾病进展和制定个性化的护理路径非常重要。在这项工作中，我们提出了一种基于深度学习的新颖而强大的方法，用于准确检测 AF。使用 RR 间隔序列，设计了一种多尺度分组卷积神经网络（MGNN）结合自注意力机制，用于自动特征提取和 AF 与非 AF 分类。在 5 折交叉验证中，获得了 97.07%的平均准确率。所提出的 MGNN 的泛化能力进一步在另外四个未见数据集上进行了独立测试，准确率分别为 92.23%、96.86%、94.23%和 95.91%。此外，对网络结构的比较表明，MGNN 不仅具有更好的检测性能，而且计算复杂度更低。总之，所提出的模型被证明是一种有效的 AF 检测器，具有在基于可穿戴设备的临床辅助诊断和长期家庭监测中应用的巨大潜力。
