Wan-Hua Lin1, Hui Wang, Oluwarotimi Williams Samuel, Gengxing Liu, Zhen Huang, Guanglin Li. 1. CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems, Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), Shenzhen 518055, People's Republic of China. Research Center for Neural Engineering at SIAT, CAS, Shenzhen 518055, People's Republic of China. Shenzhen College of Advanced Technology, UCAS, Shenzhen 518055, People's Republic of China.
Abstract
OBJECTIVE: The accuracy of cuffless and continuous blood pressure (BP) estimation has been improved, but it is still unsatisfactory for clinical uses. This study was designed to further increase BP estimation accuracy. APPROACH: In this study, a number of new indicators were extracted from photoplethysmogram (PPG) recordings and a linear regression method was used to construct BP estimation models based on the PPG indicators and pulse transit time (PTT). The performance of the BP estimation models was evaluated by the PPG recordings from 22 subjects when they performed mental arithmetic stress and Valsalva's manoeuvre tasks that could induce BP fluctuations. MAIN RESULTS: Our results showed that the best PPG-based BP estimation model could achieve a decrease of 0.31 ± 0.08 mmHg in systolic BP (SBP) and 0.33 ± 0.01 mmHg in diastolic BP (DBP) on estimation errors of grand absolute mean (GAM) and standard deviation (GSD) in comparison to the previously reported PPG-based methods. The best estimation model based on the combination of PPG and PPT could achieve a decrease (GAM & GSD) of 0.81 ± 0.95 mmHg in SBP and 0.75 ± 0.54 mmHg in DBP in comparison to the PPT-based methods. SIGNIFICANCE: The findings suggest that the newly proposed PPG indicators would be promising for improving the accuracy of continuous and cuffless BP estimation.
OBJECTIVE: The accuracy of cuffless and continuous blood pressure (BP) estimation has been improved, but it is still unsatisfactory for clinical uses. This study was designed to further increase BP estimation accuracy. APPROACH: In this study, a number of new indicators were extracted from photoplethysmogram (PPG) recordings and a linear regression method was used to construct BP estimation models based on the PPG indicators and pulse transit time (PTT). The performance of the BP estimation models was evaluated by the PPG recordings from 22 subjects when they performed mental arithmetic stress and Valsalva's manoeuvre tasks that could induce BP fluctuations. MAIN RESULTS: Our results showed that the best PPG-based BP estimation model could achieve a decrease of 0.31 ± 0.08 mmHg in systolic BP (SBP) and 0.33 ± 0.01 mmHg in diastolic BP (DBP) on estimation errors of grand absolute mean (GAM) and standard deviation (GSD) in comparison to the previously reported PPG-based methods. The best estimation model based on the combination of PPG and PPT could achieve a decrease (GAM & GSD) of 0.81 ± 0.95 mmHg in SBP and 0.75 ± 0.54 mmHg in DBP in comparison to the PPT-based methods. SIGNIFICANCE: The findings suggest that the newly proposed PPG indicators would be promising for improving the accuracy of continuous and cuffless BP estimation.