A Blood Pressure Prediction Method Based on Imaging Photoplethysmography in combination with Machine Learning. (February 2021) - Rong, Meng; Li, Kaiyang
Highlights: This paper proposed a non-contact blood pressure prediction system based on IPPG technology. The system has a whole set of BP detection steps, including video acquisition, signal extraction, filtering, feature extraction and selection, feature training and BP predicting. The experimental results fully comply with international standards. Compared with the traditional BP measurement devices, the system has the advantages of simple operation, comfortable use and continuous measurement. At the same time, compared with the current studies in the NCBP field, by comparing various machine learning methods, we found the most suitable model for blood pressure prediction under the condition of small sample size. The analysis results are more convincing than using a single algorithm. We simplified the experimental equipment of NCBP. Through experiments with different light intensity, we proved that under the condition of sufficient light, NCBP measurement does not need additional light source. Only one camera is needed to achieve blood pressure prediction. In addition, this paper innovatively proposes a highly robust peak detection algorithm and a heat map-based ROI selection method. These two aspects are key steps in the IPPG technology. The system has the potential of replacing the traditional cuff-based sphygmomanometers, and has guiding significance to the future development of blood pressure measurement devices. Abstract: This paper proposes a non-contact blood pressure implement (NCBP) system based on imaging photoplethysmography (IPPG) The system collects facial videos through a webcam under ambient light, and extracts pulse wave signals from the videos by means of IPPG technology. From the signals (also called IPPG signals), we extracted 26 features for estimating blood pressure (BP), and trained them through four machine learning algorithms. Finally, we selected the most accurate model for blood pressure prediction. By experimenting on 191 volunteers and comparing four models, support vector regression (SVR) is the best model for predicting blood pressure. The results of SVR are that the standard deviation (STD) and mean absolute error (MAE) of systolic blood pressure (SBP) are 3.35 mmHg, 9.97 mmHg, and those of diastolic blood pressure (DBP) are 2.58 mmHg, 7.59 mmHg respectively. We conclude that through our proposed system based on IPPG technology, blood pressure can be accurately predicted in a non-contact way. In addition, this paper proposes two new methods, the region of interest (ROI) selection method based on colormaps and robust peak extraction method, which solve the key steps in IPPG technology. Finally, we discussed the influence of light intensity on the experiment, and simplified the NCBP experimental device. The system has the potential of replacing the traditional cuff-based sphygmomanometers, and has guiding significance to the future development of blood pressure measurement devices.