Deep Learning for Non-Invasive Blood Pressure Monitoring: Model Performance and Quantization Trade-Offs

The development of non-invasive blood pressure monitoring systems remains a critical challenge, particularly in resource-constrained settings. This study proposes an efficient deep learning framework integrating Edge Artificial Intelligence for continuous blood pressure estimation using photoplethysmography (PPG) signals. We evaluate three architectures: a residual-enhanced convolutional neural network, a transformer-based model, and an attentive BPNet. Using the MIMIC-IV waveform database, we implement a signal processing pipeline with adaptive filtering, statistical normalization, and peak-to-peak alignment. Experiments assess varying temporal windows (10 s, 20 s, 30 s) to optimize predictive accuracy and computational efficiency. Attentive BPNet achieves the best performance, with systolic blood pressure (SBP) estimation yielding a mean absolute error (MAE) of 6.36 mmHg, diastolic blood pressure (DBP) an MAE of 4.09 mmHg, and mean arterial pressure (MBP) an MAE of 4.56 mmHg. Post-training quantization reduces the model size by 90.71% (to 0.13 MB), enabling deployment on Edge devices. These findings demonstrate the feasibility of deploying deep learning-based continuous blood pressure monitoring on edge devices. The proposed framework provides a scalable and computationally efficient solution, offering real-time, accessible monitoring that could enhance hypertension management and optimize healthcare resource utilization.