Physics Informed Neural Network for Continuous and Cuffless Arterial Blood Pressure

Continuous blood-pressure tracking is a key goal of healthcare AI, yet cuffs are still the norm in clinics and are unusable for this. We present a cuffless model that couples a Physics-Informed Neural Network (PINN) with basic hemodynamic laws to estimate full arterial pressure waveforms, from the aorta to the fingertip, using only an approximate inflow waveform Q(t) and a fingertip photoplethysmography (PPG) and electrocardiogram (ECG) signal. The network contains the Windkessel and 1-D Navier-Stokes models directly in its loss, so it learns realistic wave travel while needing no patient-specific calibration. Trained on records from the MIMIC-III Waveform Database, the network reproduces the expected changes in pulse shape along the arterial path. These results show that combining first-principles physics with deep learning could be a reliable, low-cost blood-pressure sensing.