Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan.
Institute of Information Management, National Chiao Tung University, Hsinchu, Taiwan.
Comput Methods Programs Biomed. 2021 May;203:106035. doi: 10.1016/j.cmpb.2021.106035. Epub 2021 Mar 10.
Automatic screening tools can be applied to detect cardiovascular diseases (CVDs), which are the leading cause of death worldwide. As an effective and non-invasive method, electrocardiogram (ECG) based approaches are widely used to identify CVDs. Hence, this paper proposes a deep convolutional neural network (CNN) to classify five CVDs using standard 12-lead ECG signals.
The Physiobank (PTB) ECG database is used in this study. Firstly, ECG signals are segmented into different intervals (one-second, two-seconds and three-seconds), without any wave detection, and three datasets are obtained. Secondly, as an alternative to any complex preprocessing, durations of raw ECG signals have been considered as input with simple min-max normalization. Lastly, a ten-fold cross-validation method is employed for one-second ECG signals and also tested on other two datasets (two-seconds and three-seconds).
Comparing to the competing approaches, the proposed CNN acquires the highest performance, having an accuracy, sensitivity, and specificity of 99.59%, 99.04%, and 99.87%, respectively, with one-second ECG signals. The overall accuracy, sensitivity, and specificity obtained are 99.80%, 99.48%, and 99.93%, respectively, using two-seconds of signals with pre-trained proposed models. The accuracy, sensitivity, and specificity of segmented ECG tested by three-seconds signals are 99.84%, 99.52%, and 99.95%, respectively.
The results of this study indicate that the proposed system accomplishes high performance and keeps the characterizations in brief with flexibility at the same time, which means that it has the potential for implementation in a practical, real-time medical environment.
自动筛查工具可用于检测心血管疾病（CVD），这是全球范围内的主要死亡原因。心电图（ECG）为基础的方法作为一种有效且非侵入性的方法，被广泛用于识别 CVD。因此，本文提出了一种深度卷积神经网络（CNN），用于使用标准的 12 导联 ECG 信号对五种 CVD 进行分类。
本研究使用了 Physiobank（PTB）ECG 数据库。首先，将 ECG 信号分段成不同的间隔（一秒、两秒和三秒），无需任何波检测，得到三个数据集。其次，作为任何复杂预处理的替代方案，原始 ECG 信号的持续时间被视为输入，并采用简单的 min-max 归一化。最后，采用十折交叉验证方法对一秒 ECG 信号进行验证，并在另外两个数据集（两秒和三秒）上进行测试。
与竞争方法相比，所提出的 CNN 获得了最高的性能，一秒 ECG 信号的准确率、灵敏度和特异性分别为 99.59%、99.04%和 99.87%。使用预训练的模型对两秒信号进行测试，得到的总体准确率、灵敏度和特异性分别为 99.80%、99.48%和 99.93%。对三秒信号进行测试的分段 ECG 的准确率、灵敏度和特异性分别为 99.84%、99.52%和 99.95%。
本研究结果表明，所提出的系统具有较高的性能，同时保持简洁的特征和灵活性，这意味着它有可能在实际的实时医疗环境中得到应用。
