An electrocardiogram (ECG) records the electrical activity of the heart; it contains rich pathological information on cardiovascular diseases, such as arrhythmia. However, it is difficult to visually analyze ECG signals due to their complexity and nonlinearity. The wavelet scattering transform can generate translation-invariant and deformation-stable representations of ECG signals through cascades of wavelet convolutions with nonlinear modulus and averaging operators. We proposed a novel approach using wavelet scattering transform to automatically classify four categories of arrhythmia ECG heartbeats, namely, nonectopic (N), supraventricular ectopic (S), ventricular ectopic (V), and fusion (F) beats. In this study, the wavelet scattering transform extracted 8 time windows from each ECG heartbeat. Two dimensionality reduction methods, principal component analysis (PCA) and time window selection, were applied on the 8 time windows. These processed features were fed to the neural network (NN), probabilistic neural network (PNN), and -nearest neighbour (KNN) classifiers for classification. The 4th time window in combination with KNN () has achieved the optimal performance with an averaged accuracy, positive predictive value, sensitivity, and specificity of 99.3%, 99.6%, 99.5%, and 98.8%, respectively, using tenfold cross-validation. Thus, our proposed model is capable of highly accurate arrhythmia classification and will provide assistance to physicians in ECG interpretation.

中文翻译：

---

小波散射变换用于心电图心跳分类

心电图（ECG）记录心脏的电活动。它包含有关心血管疾病（例如心律不齐）的丰富病理信息。但是，由于ECG信号的复杂性和非线性，很难对其进行可视化分析。小波散射变换可以通过具有非线性模量和平均算子的小波卷积级联来生成ECG信号的平移不变和变形稳定表示。我们提出了一种使用小波散射变换自动将心律失常心电图心律分为四类的新方法，即非异位（N），室上性异位（S），室性异位（V）和融合（F）搏动。在这项研究中，小波散射变换从每个ECG心跳中提取了8个时间窗口。二维降维方法 主成分分析（PCA）和时间窗口选择应用于8个时间窗口。这些经过处理的特征被馈送到神经网络（NN），概率神经网络（PNN）和-用于分类的最近邻居（KNN）分类器。结合KNN的第四个时间窗口（）通过十倍交叉验证，以平均准确度，阳性预测值，敏感性和特异性分别达到99.3％，99.6％，99.5％和98.8％的最佳性能。因此，我们提出的模型能够进行高度准确的心律失常分类，并将为医生的心电图解释提供帮助。