IEEE J Biomed Health Inform. 2022 Oct;26(10):4987-4995. doi: 10.1109/JBHI.2022.3191754. Epub 2022 Oct 4.
With the popularity of the wireless body sensor network, real-time and continuous collection of single-lead electrocardiogram (ECG) data becomes possible in a convenient way. Data mining from the collected single-lead ECG waves has therefore aroused extensive attention worldwide, where early detection of atrial fibrillation (AF) is a hot research topic. In this paper, a two-channel convolutional neural network combined with a data augmentation method is proposed to detect AF from single-lead short ECG recordings. It consists of three modules, the first module denoises the raw ECG signals and produces 9-s ECG signals and heart rate (HR) values. Then, the ECG signals and HR rate values are fed into the convolutional layers for feature extraction, followed by three fully connected layers to perform the classification. The data augmentation method is used to generate synthetic signals to enlarge the training set and increase the diversity of the single-lead ECG signals. Validation experiments and the comparison with state-of-the-art studies demonstrate the effectiveness and advantages of the proposed method.
随着无线体域网的普及，以方便的方式实时连续采集单导联心电图 (ECG) 数据成为可能。因此，从采集到的单导联 ECG 波中进行数据挖掘引起了全球的广泛关注，其中心房颤动 (AF) 的早期检测是一个热门研究课题。在本文中，提出了一种双通道卷积神经网络结合数据增强方法，用于从单导联短 ECG 记录中检测 AF。它由三个模块组成，第一个模块对原始 ECG 信号进行去噪，生成 9 秒 ECG 信号和心率 (HR) 值。然后，将 ECG 信号和 HR 速率值输入卷积层进行特征提取，然后通过三个全连接层进行分类。使用数据增强方法生成合成信号来扩充训练集并增加单导联 ECG 信号的多样性。验证实验和与最先进研究的比较证明了所提出方法的有效性和优势。
