A Real-Time QRS Detection Algorithm Based on Energy Segmentation for Exercise Electrocardiogram

Motion artifact is widely present in exercise electrocardiogram (ECG) signal, which is an important factor affecting the accuracy of QRS complex detection. A simple-fast QRS detection algorithm based on energy segmentation is proposed, which is suitable for exercise ECG measured by the wearable monitoring device. The proposed method mainly consists of four parts: the 15-25 Hz band-pass filter, the calculation of segmentation energy, the moving average filter and the detection of R-peaks. To enhance the information of the QRS complex, a band-pass filter is used to eliminate noises and suppress unwanted P/T waves. The information of R-R interval and QRS duration is used to energy segmentation, and moving average filter is used to obtain the adaptive energy threshold. Then, an algorithm combined adaptive energy threshold with amplitude threshold is used for QRS detection. The MIT-BIH arrhythmia database and the European ST-T database are chosen to value the correctness of the algorithm. The motion artifact contaminated ECG database is chosen to value the robustness. Exercise ECG signals obtained by lab are used to value the practicality of the algorithm. The simulation results show that the QRS detection algorithm has a sensitivity of 99.36%, a positive predictivity of 99.78%, an accuracy of 99.14%, and performs well even under ECG signal contaminated by strong motion artifacts.

Automated summary

A simple-fast QRS detection algorithm based on energy segmentation is proposed in this study, which is suitable for exercise ECG and performs well even under ECG signal contaminated by strong motion artifacts, showing high sensitivity, positive predictivity, and accuracy.