论文标题
通过一维卷积神经网络进行ECG信号分类的新方法
A Novel Method for ECG Signal Classification via One-Dimensional Convolutional Neural Network
论文作者
论文摘要
本文通过1D卷积神经网络（CNN）提出了一种基于新型分割策略的端到端ECG信号分类方法，以帮助ECG信号的分类。 ECG分割策略名为R-R-R策略（即，在当前R峰之前和之后，在R峰之间保留ECG数据），以将原始的ECG数据分割为段，以训练和测试1D CNN模型。新型策略在更大程度上模仿了扫描ECG的医师，并最大程度地提高了ECG段的固有信息。通过48个MIT-BIH心律失常数据库记录的ECG信号验证了5级和6级分类模型的性能。 As the heartbeat types are divided into 5 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, and paced beat) in the MIT-BIH, the best classification accuracy, the area under the curve (AUC), the sensitivity and the F1-score reach 99.24%, 0.9994, 0.99 and 0.99, respectively. As the heartbeat types are divided into 6 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, paced beat and other beats) in the MIT-BIH, the beat classification accuracy, the AUC, the sensitivity, and the F1-score reach 97.02%, 0.9966, 0.97, and 0.97, respectively.同时，根据医学仪器进步协会（AAMI）的建议做法，心跳类型分为5个类（即正常的节拍，上室外异位节拍，心室异位节拍，融合节拍，融合节拍和无分类的节拍）分别为0.97。实验结果表明，所提出的方法比最先进的方法获得了更好的性能。
This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) for segmenting the original ECG data into segments in order to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments. The performance of the classification models for 5-class and 6-class are verified with ECG signals from 48 records of the MIT-BIH arrhythmia database. As the heartbeat types are divided into 5 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, and paced beat) in the MIT-BIH, the best classification accuracy, the area under the curve (AUC), the sensitivity and the F1-score reach 99.24%, 0.9994, 0.99 and 0.99, respectively. As the heartbeat types are divided into 6 classes (i.e., normal beat, left bundle branch block beat, right bundle branch block beat, ventricular ectopic beat, paced beat and other beats) in the MIT-BIH, the beat classification accuracy, the AUC, the sensitivity, and the F1-score reach 97.02%, 0.9966, 0.97, and 0.97, respectively. Meanwhile, according to the recommended practice from the Association for Advancement of Medical Instrumentation (AAMI), the heartbeat types are divided into 5 classes (i.e., normal beat, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unclassifiable beats), the beat classification accuracy, the sensitivity, and the F1-score reach 97.45%, 0.97, and 0.97, respectively. The experimental results show that the proposed method achieves better performance than the state-of-the-art methods.
