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.

中文翻译：

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使用连续小波变换和卷积神经网络的自动心电图分类

早期发现心律失常并进行有效治疗可以预防心血管疾病 (CVD) 导致的死亡。在临床实践中，通过逐次检查心电图（ECG）来做出诊断，但这通常既费时又费力。在论文中，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ECG信号以获得不同的时频分量，CNN用于从上述时频分量组成的2D-scalogram中提取特征。考虑到周围的 R 峰间期（也称为 RR 间期）对心律失常的诊断也很有用，提取 4 个 RR 间期特征并结合 CNN 特征输入到全连接层进行 ECG 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 分数和准确度方面的总体性能分别达到 70.75%、67.47%、68.76% 和 98.74%。与现有方法相比，我们方法的整体 F1-score 提高了 4.75~16.85%。由于我们的方法简单且准确度高，因此有可能用作临床辅助诊断工具。