Research On Heart Rate Monitoring Using PPG In Intensive Exercise

Heart rate is a key physiological indicator of human body.The monitoring of heart rate can effectively prevent the occurrence of some cardiovascular diseases,and has important physiological significance.ECG signal and Photoplethysmography(PPG)signal are mainly used in heart rate monitoring at present.The measurement of ECG signal needs to use wet electrode affixed to the chest,which brings great inconvenience to users.The intensity change of Photoplethysmography signal is mainly caused by the change of arterial blood volume due to heart beat.Therefore,the heart rate can be calculated by Photoplethysmography signal.It is convenient and comfortable to monitor heart rate by Photoplethysmogra-phy signal.However,because the motion artifact interferes the optical path difference of pulse wave,Photoplethysmography signal will be added by the noise,then the subsequent procedure of heart rate calculation will be affected.For this reason,it is necessary to research the heart rate monitoring using Photoplethysmography signal under intensive exercise.In consideration of heart rate monitoring based on Photoplethysmo-graphy signal during intensive exercise,the Photoplethysmography signal denoising algorithm and the method of heart rate calculation are investigated in depth,and the method of heart rate monitoring scheme in motion is improved.The scheme includes the algorithm of motion artifact cancellation,and the selection of spectral peaks and the calculation method of heart rate.The new scheme is validated by experiments.The main contents and contributions of this dissertation are as follows:1.The heart rate monitoring method based on Photoplethysmography signal is studied.Motion noise cancellation algorithms are analyzed,including adaptive filtering algorithm,singular spectrum analysis algorithm and wavelet transform denoising algorithm.The calculation methods of heart rate in time domain and frequency domain are studied.2.Three improved motion artifact cancellation algorithms are proposed.Firstly,based on the adaptive filtering method,the structure of the filter and the reference signal are improved.Secondly,an algorithm for noise cancellation using sparse signal reconstruction and iterative method with adaptive thresholding is proposed.Thirdly,based on the characteristics of adaptive filtering and singular spectrum analysis,the effective combination of the two methods is studied.3.Based on frequency domain,two methods of peak selection and heart rate estimation are designed.An algorithm for peak selection and heart rate estimation based on support vector machine(SVM)model and motion disturbance is proposed,and the performance of the algorithm is compared and analyzed.4.An experiment of heart rate monitoring based on Photoplethysmo-graphy signal is designed.Comparing the proposed algorithm with TROIKA and COMB,and simulated on the open data sets and self-collected data sets,the experimental results on open data sets show that the average absolute error is 1.68 BPM and the average absolute error percentage is 1.18%.Then,it can be seen that the proposed algorithm framework in this dissertation has good effect in monitoring heart rate in motion,and has an advanced and application value in some degree.