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.
Physics Informed Neural Network for Continuous and Cuffless Arterial Blood Pressure / Delrio, Federico; Randazzo, Vincenzo; Cirrincione, Giansalvo; Pasero, Eros. - ELETTRONICO. - (2025), pp. 143-148. (Intervento presentato al convegno 2025 IEEE International Conference on Artificial Intelligence for Learning and Optimization (ICoAILO) tenutosi a Bali (Idn) nel 07-09 August 2025) [10.1109/icoailo66760.2025.11156047].
Physics Informed Neural Network for Continuous and Cuffless Arterial Blood Pressure
Delrio, Federico;Randazzo, Vincenzo;Pasero, Eros
2025
Abstract
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.| File | Dimensione | Formato | |
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https://hdl.handle.net/11583/3003672
			
		
	
	
	
			      	