Journal of Medicine and Health Research

Hypertension, a prevalent global health concern, necessitates accurate and non-invasive blood pressure estimation techniques for effective monitoring and management. This paper proposes a novel machine learning approach utilizing Photo plethysmography (PPG) signals for precise blood pressure estimation. PPG signals, obtained conveniently through wearable devices, offer valuable physiological information related to cardiovascular activity. Leveraging advanced machine learning algorithms, including deep learning architectures and feature extraction methods, our proposed technique aims to establish a robust model for blood pressure estimation using facial image analysis. The methodology involves preprocessing PPG signals, extracting relevant features, and employing sophisticated machine learning models for regression analysis. The evaluation of this novel approach involves comprehensive experimentation with diverse datasets, ensuring its efficacy across various demographic groups and conditions. Results demonstrate promising accuracy and reliability in estimating blood pressure values, suggesting the potential for practical implementation in healthcare settings. The proposed technique showcases a promising avenue for non-invasive and accessible blood pressure monitoring, contributing significantly to personalized healthcare and continuous health monitoring systems.