A Hybrid Deep Learning Approach for Cuff-less Noninvasive Continuous Blood Pressure Estimation Based on Photoplethysmography

Conventional deep learning architectures do not adequately address the requirements of wearable high-precision medical devices such as blood pressure (BP) monitors. This paper presents a novel hybrid deep learning architecture that leverages advancements in sensors and signal processing modules for cuffless and continuous BP monitoring devices, emphasizing enhanced precision in an energy constrained system. The proposed architecture comprises a combination of a convolutional neural network and a bidirectional gated recurrent unit. The proposed model adopts a data-driven end-to-end approach to directly process raw photoplethysmography (PPG) signals, enabling simultaneous estimation of systolic BP and diastolic BP without the need for feature extraction. Performance evaluation was conducted using the Multiparameter Intelligent Monitoring in Intensive Care II dataset, yielding small mean errors of 0.664 mmHg and −0.028 mmHg for the estimated and reference SBP and DBP, respectively.