Cuffless Continuous BP Monitoring from Photoplethysmography signals using UNET and Attention Mechanism

Blood pressure (BP), a vital sign in cardiovascular disease (CVD) monitoring, is traditionally measured invasively in critically ill patients or using cuff-based devices. Continuous monitoring of cardiovascular signs is crucial for early intervention and diagnosis, facilitated by miniaturized wearable technologies to mitigate further complications. In this study, we propose a modified UNET architecture with attention-based deep learning (DL) for continuous non-invasive BP estimation using only photoplethysmography (PPG) signals. The model incorporates a 1D convolutional neural network to handle sequential 1D data, enabling the capture and reconstruction of essential features for robust estimation. Attention blocks are employed to selectively collect information at different network stages, combining skip connections from the encoding and decoding paths to focus on significant features while filtering extraneous information. Additionally, the model operates in an autoencoder mode, facilitating feature extraction from intermediate layers and learning compact and meaningful representations of input data. Rigorous evaluation demonstrates that the proposed model achieves mean absolute errors (MAE) of 4.661 mmHg and 2.574 mmHg for systolic BP and diastolic BP, respectively. These results are comparable to existing methods and satisfy international standards, such as the BHS and AAMI guidelines for non-invasive BP monitoring in wearable technology. This research contributes to the advancement of accurate and non-invasive BP estimation, enabling early detection and intervention for improved cardiovascular health monitoring.