Abstract
This study proposed a measurement platform for continuous blood pressure estimation based on dual photoplethysmography (PPG) sensors and a back propagation neural network (BPNN) 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), heart rate (HR), and pulse wave analysis (PWA); these indicators were then fed into the BPNN to calculate blood pressure. The hardware of the experiment comprised 2 PPG components (i.e., Raspberry Pi 3 Model B and analog-to-digital converter [MCP3008]), which were connected using a serial peripheral interface. The BPNN 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) and diastolic blood pressure (DBP). To increase the robustness of the BPNN 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 and standard deviation of SBP were2.23±2.24 mmHg, with a mean squared error of 3.15 mmHg. The mean and standard deviation of DBP was3.5±3.53 mmHg, with a mean squared error of 4.96 mmHg. The proposed real-time blood pressure measurement system exhibited a mean accuracy of 98.22% and 95.58% for SBP and DBP, respectively.
Highlights
Hypertension remains a major factor in cardiovascular disease in Taiwan, and it is a considerable global health concern
Three experimental samples were extracted from each participant, and each sample had 10 eigenvalues; 300 samples were used for training the back propagation neural network (BPNN) model
The 10 eigenvalues collected from the dual PPG measurement, heart rate (HR), pulse transit time (PTT), pulse wave velocity (PWV), peak and trough values of dual PPGs, amplitude modulation (AM) of the dual PPG during measurement, and perfusion index (PI) were input into the BPNN for training
Summary
Hypertension remains a major factor in cardiovascular disease in Taiwan, and it is a considerable global health concern. Hypertension is a crucial indicator of whether the human heart is functioning normally. The related treatment process is long and challenging, and people still have extremely low awareness of hypertension. Research has revealed that in those aged between 19 and 44 years, the prevalence of prehypertension is higher than the proportion of patients with hypertension. The age trend is decreasing, suggesting that cardiovascular disease is no longer a concern only for older adults. Factors such as family medical history, eating habits, living habits, work stress, fatigue, temperature, and aging are all closely related to cardiovascular disease
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