Employing machine learning techniques for non-invasive blood pressure classification using photoplethysmography signals.

Regulating blood pressure (BP) effectively is vital for maintaining health and ensuring survival, yet elevation and fluctuation in BP can lead to significant health threats. Traditional methods for measuring BP, such as cuff-based and invasive procedures, can be cumbersome and do not allow for continuous measurements. In response to these challenges, our research focuses on enhancing non-invasive BP monitoring by leveraging photoplethysmography (PPG) signals in conjunction with sophisticated machine learning (ML) techniques. Our research analyzed PPG data from a diverse cohort of subjects, ranging in age from 21 to 86, including both individuals in good health and those with underlying health conditions. The analysis involved rigorous preprocessing and feature extraction processes. To enhance computational efficiency and mitigate the risk of overfitting, we applied four distinct feature selection strategies. The features identified by each method were then utilized in five ML classification models using k-fold cross validation to differentiate BP across four categories. Our findings indicate that the ensemble-based extra trees classifier (ETC) model, combined with the SelectFromModel feature selection approach, achieved remarkable accuracy of 91.13% on the test dataset. The F1 scores for the four BP categories were 93.94%, 89.31%, 88.25%, and 88.33%, respectively. Given the imbalance in dataset, we adopted two strategies for train data balancing in the second phase of our study, which boosted the test accuracy to 93.25%. The improved F1 scores for the four BP categories reached 95.34%, 90.80%, 94%, and 91.11%, respectively, underscoring the efficacy of our method in BP classification and its potential for broader application.