Evaluation of heart rate detection algorithms at different motion rates(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2020年第11期
- Page:
- 1436-1440
- Research Field:
- 医学信号处理与医学仪器
- Publishing date:
Info
- Title:
- Evaluation of heart rate detection algorithms at different motion rates
- Author(s):
- DUAN Bo; LI Jianqing; CAI Zhipeng; SUN Qi; LIU Chengyu
- School of Instrumental Science and Engineering, Southeast University, Nanjing 210096, China
- Keywords:
- Keywords: wearable device motion signal heart rate QRS detection algorithm
- PACS:
- R318
- DOI:
- DOI:10.3969/j.issn.1005-202X.2020.11.017
- Abstract:
- Abstract: The threat of cardiovascular and cerebrovascular diseases to human health is becoming more and more serious, and the real-time heart rate detection and early warning of excessive heart rate are becoming increasingly important. However, the existing researches on wearable electrocardiogram (ECG) devices and heart rate detection algorithms are mainly applied to clinical data and resting-state data, and the related research on motion state is not comprehensive. Five volunteers are enrolled in an experiment which is carried out by the combination of wearable ECG clothing and inertial navigation instrument. A total of 433 real-time data are obtained, and the correlation between speed and heart rate is analyzed with manual labeling results as true values. Meanwhile, the experimental data are divided into blocks with 1.5 m/s as the boundary point, and 5 kinds of QRS detection algorithms, namely Pan & Tompkins algorithm, Hamilton & Tompkins algorithm, JQRS algorithm, Sixth-power algorithm and Difference operation method (DOM), are adopted to detect the heart rate in the low-speed and high-speed intervals, and the detection errors are compared. The results show that there is a positive correlation between speed and heart rate. Among the 5 algorithms, difference operation method has the highest heart rate detection accuracy and that sixth-power algorithm has the worst results, which indicates that DOM is suitable for the applications related to wearable exercise ECG.
Last Update: 2020-12-02