BiGRU-attention for Continuous blood pressure trends estimation through single channel PPG

Background Physiological parameter monitoring based on photoplethysmography (PPG) detection has the advantage of fast, portable, and non-invasive. Changes in the morphology of the PPG waveform can reflect the effect of arterial elasticity changes on blood pressure (BP). However, machine learning models and non-recurrent neural network models typically ignore the time-dependency of continuous PPG data, leading to the decrease of accuracy or the increased calibration frequency. Objective This paper proposes a BiGRU model with attention to estimate BP trends, which uses a single-channel PPG signal combined with demographic information to estimate continuous BP trends point-by-point and to discuss the impact of calibration cycle. Methods This paper selects 15 typical subjects from two groups with/without cardiovascular disease (CVD) and evaluates the model performance. Regarding the calibration frequency problem, we set two modes of non-calibration and calibration to validate the results of blood pressure trends estimation. Results In the calibration mode, the estimation errors (ME +/- STD) of SBP for CVD/non-CVD groups are 0.91 +/- 10.58 mmHg/0.17 +/- 10.06 mmHg respectively, and DBP are 0.34 +/- 5.28 mmHg/-0.19 +/- 5.36 mmHg; in the non-calibration mode, the estimation errors of SBP for CVD/non-CVD groups are 0.27 +/- 9.87 mmHg/-0.82 +/- 9.92 mmHg respectively, and DBP are -0.63 +/- 3.28 mmHg/0.80 +/- 4.93 mmHg. Conclusions The results show that the proposed model has high accuracy in estimating BP levels, which is expected to achieve real-time, long-term continuous BP trends monitoring in wearable devices.