Cuff-less blood pressure monitoring via PPG signals using a hybrid CNN-BiLSTM deep learning model with attention mechanism.

Blood pressure (BP) serves as a fundamental indicator of cardiovascular health, measuring the force exerted by circulating blood against arterial walls during each heartbeat. This paper introduces an advanced deep learning framework for precise, non-invasive BP estimation via photoplethysmography (PPG) signals, addressing critical limitations in traditional, cuff-based BP measurement methods. Traditional methods, while reliable, are limited by their inability to provide continuous data, posing challenges for proactive health management. In contrast, PPG-based BP estimation facilitates continuous monitoring, crucial for wearable health technologies and real-time applications. Our proposed model leverages a hybrid architecture of convolutional neural networks (CNNs), bidirectional long short-term memory (BiLSTM) layers, and an attention mechanism, enabling refined spatial and temporal feature extraction to enhance BP estimation accuracy. This approach is validated on an extensive dataset of 2064 patients from the MIMIC-II database, marking a significant increase in sample size over prior studies and thereby strengthening model robustness and generalizability. Through meticulous preprocessing steps, the model achieved an impressive mean absolute error (MAE) of 1.88 for systolic blood pressure (SBP) and 1.34 for diastolic blood pressure (DBP) across 5-fold cross-validation. These findings underscore the potential of integrating PPG and deep learning as a viable, scalable solution for wearable BP monitoring, providing a foundation for further advancement in accessible, non-invasive cardiovascular health monitoring technologies.