A hierarchical method based on weighted extreme gradient boosting in ECG heartbeat classification. (April 2019)

• Record Type: Journal Article
• Authors: Shi, Haotian; Wang, Haoren; Huang, Yixiang; Zhao, Liqun; Qin, Chengjin; Liu, Chengliang
• Is Part Of: Computer methods and programs in biomedicine. Volume 171(2019)
• Page Start: 1
• Page End: 10
• Publication Date: 2019-04
• Subjects: Electrocardiogram (ECG) -- Heartbeat classification -- Extreme gradient boosting (XGBoost) -- Hierarchical classifier; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2019.02.005
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Extreme gradient boosting is first introduced and utilized for single heartbeat classification. A weighted XGBoost classifier for unbalanced heartbeat dataset with multiple classes is presented. A hierarchical classifier based on weighted extreme gradient boosting and threshold classifiers is constructed. Recursive feature elimination is employed for feature selection from a large number of features. Both high positive predictive value for N class and high sensitivity for abnormal classes are provided, which is practical for clinical diagnosis. Abstract: Background and objective: 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. Methods: 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. Results: 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. Conclusions: 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.