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Camera-based Blood Pressure Estimation via Windkessel Model and Waveform Features
IEEE Transactions on Instrumentation and Measurement ; : 1-1, 2022.
Article in English | Scopus | ID: covidwho-2192095
ABSTRACT
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 in the Windkessel model. For a comprehensive evaluation, the Chiao Tung Blood Pressure (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 of 6.48 mmHg and 5.06 mmHg for SBP and DBP, respectively. The performance achieves a “B”grade according to the validation protocol from the British Hypertension Society for both SBP and DBP. IEEE
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: IEEE Transactions on Instrumentation and Measurement Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: IEEE Transactions on Instrumentation and Measurement Year: 2022 Document Type: Article