Statistical performance on ECG test dataset.

An electrocardiograph (ECG) is widely used in diagnosis and prediction of cardiovascular diseases (CVDs). The traditional ECG classification methods have complex signal processing phases that leads to expensive designs. This paper provides a deep learning (DL) based system that employs the convolutional neural networks (CNNs) for classification of ECG signals present in PhysioNet MIT-BIH Arrhythmia database. The proposed system implements 1-D convolutional deep residual neural network (ResNet) model that performs feature extraction by directly using the input heartbeats. We have used synthetic minority oversampling technique (SMOTE) that process class-imbalance problem in the training dataset and effectively classifies the five heartbeat types in the test dataset. The classifier’s performance is evaluated with ten-fold cross validation (CV) using accuracy, precision, sensitivity, F1-score, and kappa. We have obtained an average accuracy of 98.63%, precision of 92.86%, sensitivity of 92.41%, and specificity of 99.06%. The average F1-score and Kappa obtained were 92.63% and 95.5% respectively. The study shows that proposed ResNet performs well with deep layers compared to other 1-D CNNs.心电图（electrocardiograph, ECG）被广泛应用于心血管疾病（cardiovascular diseases, CVDs）的诊断与预测。传统心电图分类方法存在复杂的信号处理流程，导致设计成本高昂。本文提出了一种基于深度学习（deep learning, DL）的系统，采用卷积神经网络（convolutional neural networks, CNNs）对PhysioNet MIT-BIH心律失常数据库中的心电图信号进行分类。所提系统采用一维卷积深度残差神经网络（1-D convolutional deep residual neural network, ResNet）模型，可直接通过输入的心跳数据完成特征提取。本文采用合成少数类过采样技术（synthetic minority oversampling technique, SMOTE）解决训练数据集的类别不平衡问题，并在测试数据集上有效实现了五种心跳类型的分类。本研究通过十折交叉验证（ten-fold cross validation, CV），以准确率、精确率、灵敏度、F1值以及Kappa系数作为评估指标，对分类器性能进行验证。本研究获得的平均准确率为98.63%，平均精确率为92.86%，平均灵敏度为92.41%，平均特异度为99.06%。所获平均F1值与Kappa系数分别为92.63%与95.5%。研究表明，相较于其他一维卷积神经网络，本文提出的残差神经网络在深层架构下表现更优。