School of Mechanical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, PR China.
Department of Cardiology, Shanghai First People's Hospital Affiliated to Shanghai Jiao Tong University, 100, Haining Road, Shanghai 200080, PR China.
Comput Methods Programs Biomed. 2019 Apr;171:1-10. doi: 10.1016/j.cmpb.2019.02.005. Epub 2019 Feb 20.
Electrocardiogram (ECG) is a useful tool for detecting heart disease. Automated ECG diagnosis allows for heart monitoring on small devices, especially on wearable devices. In order to recognize arrhythmias automatically, accurate classification method for electrocardiogram (ECG) heartbeats was studied in this paper.
Based on weighted extreme gradient boosting (XGBoost), a hierarchical classification method is proposed. A large number of features from 6 categories are extracted from the preprocessed heartbeats. Then recursive feature elimination is used for selecting features. Afterwards, a hierarchical classifier is constructed in classification stage. The hierarchical classifier is composed of threshold and XGBoost classifiers. And the XGBoost classifiers are improved with weights.
The method was applied to an inter-patient experiment conforming AAMI standard. The obtained sensitivities for normal (N), supraventricular (S), ventricular (V), fusion (F), and Unknown beats (Q) were 92.1%, 91.7%, 95.1%, and 61.6%. Positive predictive values of 99.5%, 46.2%, 88.1%, and 15.2% were also provided for the four classes.
XGBoost was improved and firstly introduced in single heartbeat classification. A comparison showed the effectiveness of the novel method. The method was more suitable for clinical application as both high positive predictive value for N class and high sensitivities for abnormal classes were provided.
心电图（ECG）是检测心脏病的有用工具。自动心电图诊断允许在小型设备上进行心脏监测，尤其是在可穿戴设备上。为了自动识别心律失常，本文研究了一种用于心电图（ECG）心拍的精确分类方法。
基于加权极端梯度提升（XGBoost），提出了一种分层分类方法。从预处理的心拍中提取来自 6 个类别的大量特征。然后，使用递归特征消除来选择特征。之后，在分类阶段构建分层分类器。分层分类器由阈值和 XGBoost 分类器组成。并且使用权重改进了 XGBoost 分类器。
该方法应用于符合 AAMI 标准的患者间实验。获得的正常（N）、室上性（S）、室性（V）、融合（F）和未知（Q）心拍的灵敏度分别为 92.1%、91.7%、95.1%和 61.6%。四个类别的阳性预测值分别为 99.5%、46.2%、88.1%和 15.2%。
改进了 XGBoost 并将其首次引入单个心拍分类中。比较表明了该新方法的有效性。该方法更适合临床应用，因为为 N 类提供了高阳性预测值，并且为异常类提供了高灵敏度。
