The study is to establish the non-contact blood pressure measurement model. We propose a novel hybrid blood pressure assessment model. This model employs digital signal processing (DSP) to process the Imaging Photoplethysmography (iPPG) signal, utilizing Support Vector Machine (SVM) classification to determine the optimal signal location through three parameters. It is then compared with a PPG device. Through a CNN-LSTM model, it aims to reconstruct the ideal iPPG signal, transforming signals from the dermal layer into radial artery signals. Based on the Beer-Lambert law, the natural logarithm of iPPG intensity is proportional to blood flow velocity. Thus, a regression model for mean arterial pressure is developed in this work using heart rate and the intensity of iPPG signals. In conclusion, statistical test results confirm the validity of this study, indicating significant potential for the future development of noncontact blood pressure monitoring.
We propose a novel continuous blood pressure monitoring system which is based on an autonomic nervous system, and which considers blood volume simultaneously since both affect blood pressure. An autonomic nervous system regulates blood pressure while blood volume is known to be proportional to the photoplethysmography (PPG) signal. To overcome the limitation of taking blood pressure using a conventional cuff inflating instrument, we designed a system which can achieve continuous blood pressure monitoring. In this research, we used a set of near-infrared light source (940nm) to create a divergent light which was collimated as a uniform beam incident to a wrist surface through a Fourier optics designed transfer lens. We found that the signals became more stable due to the uniform illumination and could be received by a detector. From the signals, we found that the blood volume when converted from blood velocity as measured by an ultrasound probe, showed a strong correlation with the signals. The heart rate variability analyzed from the signals, including time-domain (HR and SDNN) and frequency-domain (LF and HF) indices, could be viewed as physical models since these indices reflect the functions of an autonomic nervous system. Moreover, the research derived regression models can estimate blood pressure. Although it is not common to assess blood pressure from the perspective of an autonomic nervous system and blood flow simultaneously, our research approach seems logical. Our results show the potential for this novel system to be used for blood pressure health monitoring.
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