Wang Tao, Lu Changhua, Sun Yining, Yang Mei, Liu Chun, Ou Chunsheng
School of Computer and Information, Hefei University of Technology, Hefei 230009, China.
Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China.
Entropy (Basel). 2021 Jan 18;23(1):119. doi: 10.3390/e23010119.
Early detection of arrhythmia and effective treatment can prevent deaths caused by cardiovascular disease (CVD). In clinical practice, the diagnosis is made by checking the electrocardiogram (ECG) beat-by-beat, but this is usually time-consuming and laborious. In the paper, we propose an automatic ECG classification method based on Continuous Wavelet Transform (CWT) and Convolutional Neural Network (CNN). CWT is used to decompose ECG signals to obtain different time-frequency components, and CNN is used to extract features from the 2D-scalogram composed of the above time-frequency components. Considering the surrounding R peak interval (also called RR interval) is also useful for the diagnosis of arrhythmia, four RR interval features are extracted and combined with the CNN features to input into a fully connected layer for ECG classification. By testing in the MIT-BIH arrhythmia database, our method achieves an overall performance of 70.75%, 67.47%, 68.76%, and 98.74% for positive predictive value, sensitivity, F1-score, and accuracy, respectively. Compared with existing methods, the overall F1-score of our method is increased by 4.75~16.85%. Because our method is simple and highly accurate, it can potentially be used as a clinical auxiliary diagnostic tool.
心律失常的早期检测和有效治疗可预防心血管疾病（CVD）导致的死亡。在临床实践中，通过逐搏检查心电图（ECG）进行诊断，但这通常既耗时又费力。在本文中，我们提出了一种基于连续小波变换（CWT）和卷积神经网络（CNN）的自动心电图分类方法。CWT用于分解心电图信号以获得不同的时频成分，CNN用于从由上述时频成分组成的二维尺度图中提取特征。考虑到周围的R波间期（也称为RR间期）对心律失常的诊断也很有用，提取了四个RR间期特征并与CNN特征相结合，输入到一个全连接层进行心电图分类。通过在MIT-BIH心律失常数据库中进行测试，我们的方法在阳性预测值、灵敏度、F1分数和准确率方面的整体性能分别达到了70.75%、67.47%、68.76%和98.74%。与现有方法相比，我们方法的整体F1分数提高了4.75%至16.85%。由于我们的方法简单且准确率高，它有可能用作临床辅助诊断工具。
