ArticlePhysical and engineering sciences in medicine2025
Exploring supervised machine learning models to estimate blood pressure using non-fiducial features of the photoplethysmogram (PPG) and its derivatives.
Article in Physical and engineering sciences in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Machine learning has proven valuable in developing photoplethysmography (PPG)-based approaches for blood pressure (BP) estimation, with many holding some promise for cuff-less BP assessment. Still, their efficacy relies on accurate and robust fiducial point detection algorithms. The present study explores the usefulness of several non-fiducial features of the PPG signal and its derivatives in estimating BP by combining well-known feature selection methods and machine learning techniques. We collected PPG recordings from 56 participants and computed fifty-seven non-fiducial features, including statistical indexes and energy operators. After implementing three feature selection algorithms (i.e., F-test, mRMR, and ReliefF), the most relevant features were employed to train four learning regression model families. We computed the mean of all absolute errors (MAE), the squared sum and the standard deviation of the errors (MSE and RMSE, respectively), and the coefficient of determination (r
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