Higher Order Derivative-Based Integrated Model for Cuff-Less Blood Pressure Estimation and Stratification Using PPG Signals

Abstract

Precise blood pressure (BP) estimation is vital for diagnosing arterial hypertension and other cardiovascular ailments. The photoplethysmogram (PPG)-based cuff-less BP measurement is an alternative to the traditional cuff-based systems. The morphological, temporal, and frequency-domain-based features have been used for BP estimation in the PPG-based BP measurement systems. However, dealing with varying signal morphology and feature dependency on the fiducial points in PPG contours remains challenging and limits the performance of the existing BP estimation algorithms, especially in wearable devices. This work presents a novel approach that considers the nonlinear features of PPG signals evaluated using higher order derivatives. In particular, the PPG signal’s third and fourth derivative contours are used to extract features, such as fractal dimension, bubble entropy (BE), Lyapunov exponent, and moving slope. Machine learning algorithms such as random forest (RF), extreme gradient boosting (XGBoost), and support vector regression (SVR) models are used for BP estimation using the nonlinear features of PPG signals. The estimated BP values are further categorized based on five broad classes based on the BP stratification criteria such as hypotension, normal, prehypertension, stage-I, and stage-II hypertension, respectively. The performance of the suggested approach is evaluated using PPG signals from three publicly available databases (multiparameter intelligent monitoring in intensive care (MIMIC)-I, II, and III). The proposed estimation approach has outperformed the recent existing algorithms and achieved a minimum value of mean absolute error (MAE) ± standard deviation (STD) in (systolic BP (SBP) and diastolic BP (DBP) values) as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${0}.{74}\pm {2}.{42}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${0}.{35} \pm {1}.{06}$ </tex-math></inline-formula> , respectively, for the MIMIC-I database. The suggested approach has also achieved grade-A on the British Hypertension Society (BHS) standard.