In this paper, we propose a data-driven model lever-aging a Limited Penetrable Weighted Visibility Graph (LPWVG) derived from the photoplethysmogram (PPG) waveform for Pulse Wave Velocity (PWV) estimation. Four distinct LPWVGs were constructed employing diverse weighted methods. Subsequently, various features have been computed and extracted from PPG images, including two-dimensional Semi-classical Signal Analysis (SCSA)-based features, frequency-based features, and shape-based features. These features were then input into different machine-learning models. The proposed approach's performance was rigorously evaluated using both in-silico and real PPG pulse wave data. The obtained results provide compelling evidence supporting the feasibility and effectiveness of the proposed method for accurate PWV estimation in biomedical applications.
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| http://dx.doi.org/10.1109/EMBC53108.2024.10782510 | DOI Listing |
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