A filter design method based on evolutionary learning for R-peak detection in portable ECG testing devices

Abstract

Portable ECG testing devices can develop individuals’ understanding of abnormal heart conditions in a timely manner and are of great value in disease prevention. R-peak detection is the first step in achieving fast and accurate ECG diagnosis. Currently, most R-peak (and QRS complex) detection algorithms target medical-grade multilead ECG measurement devices and cannot be applied to portable single-lead ECG measurement bracelets in low computing power and strong noise environments. Therefore, a filter design method for ECG R-peak detection was proposed in this paper to improve ECG quality with limited hardware resources. Besides, evolutionary learning was adopted in the filter design of the R-peak detection algorithm to reveal the optimal filter through the good global search ability of the genetic algorithm and the morphological features of the ECG. Finally, the precision, recall, and F1 score on the self-built ECG dataset reached 97.03%, 98.49%, and 97.67%, respectively, and the superiority of the method in some performance metrics compared with other excellent filters was verified.