BiGRU-attention for Continuous Blood Pressure Trends Estimation Through Single Channel PPG.
Computers in Biology and Medicine(2024)
摘要
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.
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关键词
Single channel PPG monitoring,BiGRU model with attention,Continuous blood pressure
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