A Deep Learning-Based Continuous Blood Pressure Measurement by Dual Photoplethysmography Signals

作     者：Chih-Ta Yen Sheng-Nan Chang Liao Jia-Xian Yi-Kai Huang 

作者机构：Department of Electrical EngineeringTaiwan Ocean UniversityKeelung202301Taiwan Department of Internal MedicineNational Taiwan UniversityYun-Lin BranchDou-Liu640Taiwan Department of Electrical EngineeringNational Formosa UniversityYunlin632Taiwan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第2期

页      面：2937-2952页

核心收录：

学科分类：0808[工学-电气工程] 1002[医学-临床医学] 0809[工学-电子科学与技术（可授工学、理学学位）] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

基　　金：This study was supported in part by the Ministry of Science and Technology MOST 108-2221-E-150-022-MY3 and Taiwan Ocean University 

主　　题：Deep learning(DL) blood pressure continuous non-invasive blood pressure measurement photoplethysmography(PGG) 

摘      要：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 *** 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 *** hardware of the experiment comprised 2 PPG components(i.e.,Raspberry Pi 3 Model B and analog-todigital converter[MCP3008]),which were connected using a serial peripheral *** DL algorithm converted the stable dual PPG signals acquired from the strictly standardized experimental process into various physiological indicators as input parameters and finally obtained the systolic blood pressure(SBP),diastolic blood pressure(DBP)and mean arterial pressure(MAP).To increase the robustness of the DL model,this study input data of 100 Asian participants into the training database,including those with and without cardiovascular disease,each with a proportion of approximately 50%.The experimental results revealed that the mean absolute error and standard deviation of SBP was 0.17±0.46 *** mean absolute error and standard deviation of DBP was 0.27±0.52 *** mean absolute error and standard deviation of MAP was 0.16±0.40 mmHg.