School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China,People's Republic of China.
Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, People's Republic of China.
Physiol Meas. 2022 Oct 31;43(10). doi: 10.1088/1361-6579/ac7938.
Automatic detection of arrhythmia based on electrocardiogram (ECG) plays a critical role in early prevention and diagnosis of cardiovascular diseases. With the increase in widely available digital ECG data and the development of deep learning, multi-class arrhythmia classification based on automatic feature extraction of ECG has become increasingly attractive. However, the majority of studies cannot accept varied-length ECG signals and have limited performance in detecting multi-class arrhythmias.In this study, we propose a multi-branch signal fusion network (MBSF-Net) for multi-label classification of arrhythmia in 12-lead varied-length ECG. Our model utilizes the complementary power between different structures, which include Inception with depthwise separable convolution (DWS-Inception), spatial pyramid pooling (SPP) Layer, and multi-scale fusion Resnet (MSF-Resnet). The proposed method can extract features from each lead of 12-lead ECG recordings separately and then effectively fuse the features of each lead by integrating multiple convolution kernels with different receptive fields, which can achieve the information of complementation between different angles of the ECG signal. In particular, our model can accept 12-lead ECG signals of arbitrary length.The experimental results show that our model achieved an overall classification F1 score of 83.8% in the 12-lead ECG data of CPSC-2018. In addition, the F1 score of the MBSF-Net performed best among the MBF-Nets which are removed the SPP layer from MBSF-Net. In comparison with the latest ECG classification algorithms, the proposed model can be applied in varied-length signals and has an excellent performance, which not only can fully retain the integrity of the original signals, but also eliminates the cropping/padding signal beforehand when dealing with varied-length signal database.MBSF-Net provides an end-to-end multi-label classification model with outperfom performance, which allows detection of disease in varied-length signals without any additional cropping/padding. Moreover, our research is beneficial to the development of computer-aided diagnosis.
基于心电图 (ECG) 的心律失常自动检测在心血管疾病的早期预防和诊断中起着关键作用。随着广泛可用的数字 ECG 数据的增加和深度学习的发展，基于 ECG 自动特征提取的多类心律失常分类变得越来越有吸引力。然而，大多数研究无法接受长短不一的 ECG 信号，并且在检测多类心律失常方面的性能有限。
在这项研究中，我们提出了一种多分支信号融合网络 (MBSF-Net)，用于 12 导联长短不一的 ECG 的多标签分类。我们的模型利用了不同结构之间的互补能力，包括具有深度可分离卷积 (DWS-Inception) 的 Inception、空间金字塔池化 (SPP) 层和多尺度融合 Resnet (MSF-Resnet)。该方法可以分别从 12 导联 ECG 记录的每个导联中提取特征，然后通过集成多个具有不同感受野的卷积核，有效地融合每个导联的特征，从而实现 ECG 信号不同角度之间信息的互补。特别是，我们的模型可以接受任意长度的 12 导联 ECG 信号。
实验结果表明，我们的模型在 CPSC-2018 的 12 导联 ECG 数据中实现了 83.8%的总体分类 F1 得分。此外，在从 MBSF-Net 中去除 SPP 层的 MBF-Nets 中，MBSF-Net 的 F1 得分表现最佳。与最新的 ECG 分类算法相比，该模型可以应用于长短不一的信号，具有出色的性能，不仅可以充分保留原始信号的完整性，还可以在处理长短不一的信号数据库时消除裁剪/填充信号。
MBSF-Net 提供了一种端到端的多标签分类模型，具有出色的性能，可以在不进行任何额外裁剪/填充的情况下检测长短不一的信号中的疾病。此外，我们的研究有助于计算机辅助诊断的发展。
