Non-contact continuous blood pressure measurement based on imaging equipment

Ying Guo; Xiaohua Liu; Lingqin Kong; Ming Liu; Yuejin Zhao; Liquan Dong

doi:10.1117/12.2540316

12 March 2020 Non-contact continuous blood pressure measurement based on imaging equipment

Ying Guo, Xiaohua Liu, Lingqin Kong, Ming Liu, Yuejin Zhao, Liquan Dong

Proceedings Volume 11438, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology; 1143802 (2020) https://doi.org/10.1117/12.2540316
Event: 2019 International Conference on Optical Instruments and Technology, 2019, Beijing, China

Abstract

An optical and non-contact continuous measurement method to detect human blood pressure through a high-speed camera is discussed in this paper. With stable ambient light, photoplethysmographic (PPG) signals of face and palm area are obtained simultaneously from the video captured by high-speed camera, whose frame rate should be higher than 100 frames per second. Pulse transit time (PTT) is measured from the R-wave distance between the two PPG signals. The Partial least squares regression( PLSR) model was established to train the samples, and the relationship between PTT and blood pressure, including intra-arterial systolic pressure (SBP) and diastolic pressure (DBP), was established to obtain blood pressure. Compared with the output of traditional sphygmomanometer, the blood pressure data collected from non-contact system has little error and meets the fitting conditions. We first proposed an accurate video-based method for non-contact blood pressure measurement using machine learning, and the average error of SBP is 0.148mmHg and of DBP is 0.359mmHg.

Citation Download Citation

Ying Guo, Xiaohua Liu, Lingqin Kong, Ming Liu, Yuejin Zhao, and Liquan Dong "Non-contact continuous blood pressure measurement based on imaging equipment", Proc. SPIE 11438, 2019 International Conference on Optical Instruments and Technology: Optoelectronic Imaging/Spectroscopy and Signal Processing Technology, 1143802 (12 March 2020); https://doi.org/10.1117/12.2540316

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