Abnormal heart rate detection based on double slope QRS beat location

Abstract

In order to improve the accuracy and reliability of abnormal heart rate detection, a method of abnormal heart rate detection based on double slope QRS beat location is proposed. Firstly, according to the singularity of QRS wave, the slope difference between the left and right sides is used for localization, and double thresholds are set to track the real-time change of the signal to ensure the robustness of localization. Secondly, wavelet coefficients are extracted as beat frequency features by wavelet transform. Finally, XGBoost algorithm is used for classification. In the case of the same data set, compared with the existing arrhythmia detection algorithm, the performance is further improved. The sensitivity of QRS detection is 99.65%, the positive prediction rate is 99.41%, and the accuracy of heart rate anomaly classification is 99.12%. This method can locate the beat more effectively, which provides a new method for the research of abnormal heart rate detection.