Chih-Ta Yen^1,*, Sheng-Nan Chang², Liao Jia-Xian³, Yi-Kai Huang³

CMC-Computers, Materials & Continua, Vol.70, No.2, pp. 2937-2952, 2022, DOI:10.32604/cmc.2022.020493

Abstract This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography (PPG) sensors and a deep learning (DL) that can be used for continuous and rapid measurement of blood pressure and analysis of cardiovascular-related indicators. The proposed platform measured the signal changes in PPG and converted them into physiological indicators, such as pulse transit time (PTT), pulse wave velocity (PWV), perfusion index (PI) and heart rate (HR); these indicators were then fed into the DL to calculate blood pressure. The hardware of the experiment comprised 2 PPG components (i.e., Raspberry Pi 3 Model B and analog-to-digital… More >

Jiancheng Zou¹, Shouyu Zhou^1,*, Bailin Ge¹, Xin Yang²

Journal of New Media, Vol.3, No.2, pp. 41-51, 2021, DOI:10.32604/jnm.2021.017764

Abstract Blood pressure is an important physiological parameter to reflect human vital signs. In order to achieve the non-contact dynamic blood pressure acquisition based on ordinary optical camera, a theoretical understanding of the functional relationship between blood pressure and pulse wave signal conduction time. And through imaging photoelectric plethysmography (IPPG), pulse wave signal conduction time of forehead and hand was obtained with ordinary optical camera. First, the pulse wave conduction time was obtained by recording the video with an ordinary optical camera. Second, real-time blood pressure values were collected. Finally, based on the relationship between blood pressure and pulse wave conduction… More >