QI Yusheng 1,2,3 , ZHANG Aihua 1,2,3 , MA Yurun 1,2,3
  • 1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, P.R.China;
  • 2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou University of Technology, Lanzhou 730050, P.R.China;
  • 3. National Demonstration Center for Experimental Electrical and Control Engineering Education, Lanzhou University of Technology, Lanzhou 730050, P.R.China;
ZHANG Aihua, Email: zhangaihua@lut.edu.cn
Export PDF Favorites Scan Get Citation

The extraction of pulse rate variability(PRV) in daily life is often affected by exercise and blood perfusion. Therefore, this paper proposes a method of detecting pulse signal and extracting PRV in post-ear, which could improve the accuracy and stability of PRV in daily life. First, the post-ear pulse signal detection system suitable for daily use was developed, which can transmit data to an Android phone by Bluetooth for daily PRV extraction. Then, according to the state of daily life, nine experiments were designed under the situation of static, motion, chewing, and talking states, respectively. Based on the results of these experiments, synchronous data acquisition of the single-lead electrocardiogram (ECG) signal and the pulse signal collected by the commercial pulse sensor on the finger were compared with the post-auricular pulse signal. According to the results of signal wave, amplitude and frequency-amplitude characteristic, the post-ear pulse signal was significantly steady and had more information than finger pulse signal in the traditional way. The PRV extracted from post-ear pulse signal has high accuracy, and the accuracy of the nine experiments is higher than 98.000%. The method of PRV extraction from post-ear has the characteristics of high accuracy, good stability and easy use in daily life, which can provide new ideas and ways for accurate extraction of PRV under unsupervised conditions.

Citation: QI Yusheng, ZHANG Aihua, MA Yurun. The study on extraction method of pulse rate variability in daily unsupervised state. Journal of Biomedical Engineering, 2019, 36(2): 298-305. doi: 10.7507/1001-5515.201804015 Copy

  • Previous Article

    A tooth cone beam computer tomography image segmentation method based on the local Gaussian distribution fitting
  • Next Article

    Automatic recognition and analysis of hemiplegia gait