Exploring the potential of five machine learning regression algorithms for noninvasive blood pressure estimation with...

Objective: Blood pressure (BP) is a vital factor for human health and survival, and its elevation and fluctuations can have dangerous consequences on an individual's well-being. Traditional BP measurement methods-including cuff-based devices and invasive arterial catheters-are unsuitable for continuous monitoring in daily life: cuffs are intermittent and uncomfortable, whereas arterial lines provide continuous data but are invasive and confined to clinical settings (e.g., ICUs/ORs). In response to this requirement, we propose a cuff-less, continuous, and noninvasive system for BP measurement using photoplethysmograph (PPG) signals and machine learning (ML) algorithms. Methods: In this investigation, we analyzed PPG signals acquired from a diverse cohort, with participants ranging in age from 21 to 86 years and including both healthy subjects and those with health conditions. The data underwent rigorous preprocessing and feature extraction procedures. To address computational efficiency and mitigate overfitting, we applied five distinct feature selection methods to refine the feature set. Subsequently, each method's selected features were independently trained and tested using five ML regression algorithms to estimate systolic blood pressure (SBP) and diastolic blood pressure (DBP). Results: Our findings reveal that the ensemble-based extra trees (ET) algorithm, coupled with the SelectFromModel feature selection approach, surpassed competing algorithms in estimative performance. The ET algorithm achieved notably low root mean squared errors (RMSEs) of 5.21 for SBP and 2.65 for DBP, demonstrating its exceptional capability in the estimation of BP. Conclusion: The proposed approach demonstrates strong potential for accurate, non-invasive BP estimation. These findings have important implications for the development of wearable and mobile health technologies that support continuous, real-time BP monitoring for the prevention and management of hypertension and cardiovascular diseases.