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

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-todigital converter [MCP3008]), which were connected using a serial peripheral interface. The 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 mmHg. The mean absolute error and standard deviation of DBP was 0.27 ± 0.52 mmHg. The mean absolute error and standard deviation of MAP was 0.16 ± 0.40 mmHg.

Cuffless and continuous blood pressure estimation from single-channel photoplethysmography signal using end-to-end deep learning models

“…Athaya et al [17] used the U-net deep learning framework and PPG raw signals as inputs to measure ABP waveforms and calculate discrete blood pressure based on the measured ABP waveform. In recent years, blood pressure measurement methods based on CNN-LSTM have become a hotspot [18][19][20][21] , with multi-layer convolution operations able to automatically and efficiently learn local features that cannot be captured by the human eye, achieving signal enhancement and noise reduction, while also better handling long sequential data. Although these methods have achieved good results, there are still many shortcomings that need to be addressed.…”