Camera-Based Blood Pressure Estimation via Windkessel Model and Waveform Features

Blood pressure (BP) is generally regarded as the vital sign most strongly correlated with human health. However, for decades, BP measurement has involved a cuff, which causes discomfort and even carries a risk of infection, given the current prevalence of COVID-19. Some studies address these problem...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2023, Vol.72, p.1-13
Main Authors: Wu, Bing-Jhang, Wu, Bing-Fei, Hsu, Chi-Po
Format: Article
Language:English
Subjects:
Algorithms
Blood pressure
Blood pressure (BP) measurement
cuffless blood pressure measurement
Datasets
Estimation
Feature extraction
Heart rate
Hypertension
Integrated circuit modeling
Monte Carlo simulation
Pressure measurement
Protocols
remote photoplethysmography (rPPG)
Robustness
rPPG feature selection
Signal processing
Waveforms
windkessel model
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Summary:Blood pressure (BP) is generally regarded as the vital sign most strongly correlated with human health. However, for decades, BP measurement has involved a cuff, which causes discomfort and even carries a risk of infection, given the current prevalence of COVID-19. Some studies address these problems using remote photoplethysmography (rPPG), which has shown great success in heart rate detection. Nevertheless, these approaches are not robust, and few have been evaluated with a sufficiently large dataset. We propose an rPPG-based BP estimation algorithm that predicts BP by leveraging the Windkessel model and hand-crafted waveform characteristics. A waveform processing procedure is presented for the rPPG signals to obtain a robust waveform template and thus extract BP-related features. Redundant and unstable features are eliminated via Monte Carlo simulation and according to their relationship with latent parameters (LSs) in the Windkessel model. For a comprehensive evaluation, the Chiao Tung BP (CTBP) dataset was constructed. The experiment was conducted over a four-week period of time to evaluate the validity period of the personalization in our system. On all the data, the proposed method outperforms the benchmark algorithms and yields mean absolute errors (MAEs) of 6.48 and 5.06 mmHg for systolic BP (SBP) and diastolic BP (DBP), respectively. The performance achieves a "B" grade according to the validation protocol from the British Hypertension Society (BHS) for both SBP and DBP.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2022.3224534