ABSTRACT
Objective:To study a classifier used to classify arrhythmia electrocardiogram (ECG) signals under the inter-patient paradigm to improve the accuracy of automatic classification and solve the limitations of manual diagnosis of arrhythmia.Methods:A SVM+XGBoost ensemble classifier with four modules including preprocessing, feature extraction, support vector machine (SVM) training and ensemble classification was constructed. ECG signal was preprocessed, and R-R interval, high-order statistics, local binary patterns and wavelet components were used as features to train independent SVM classifiers. Then, XGboost algorithm was used to integrate independent SVM classifiers and output arrhythmia classification results. The integrated classifiers were trained and tested on MIT-BIH database.Results:The overall classification accuracy of the ensemble classifier for arrhythmia was 0.867 and the average sensitivity was 0.782.Conclusions:The proposed ensemble classifier can realize automatic and accurate classification of arrhythmia ECG signals under the inter-patient paradigm, and can be used for clinical auxiliary diagnosis.