Personalized and Explainable Blood Pressure Estimation from PPG via Hybrid CNN--Morphological Features

A physiology-guided hybrid framework combining a convolutional neural network branch with a morphology-prior branch estimated blood pressure from photoplethysmography (PPG) with mean absolute errors of 3.77 mmHg for systolic BP and 2.36 mmHg for diastolic BP, according to the arXiv paper 2609.13190v1. Evaluated on a subset of the MIMIC-III database under a subject-specific testing protocol, the approach improved on a personalized CNN-only baseline by 43.7% for systolic and 32.4% for diastolic BP. SHAP-based analysis confirmed the morphology-prior features aligned with individual vascular characteristics, supporting per-subject interpretability.

arXiv:2609.13190v1 Announce Type: new Abstract: Continuous cuffless blood pressure BP monitoring using photoplethysmography PPG offers a promising solution for personalized healthcare. However, existing methods have two major limitations. Handcrafted feature-based approaches rely on precise fiducial point detection and are limited to short-term analysis, while deep learning models, despite their accuracy, often operate as black boxes with limited physiological interpretability. To address these challenges, we propose a physiology-guided hybrid framework for personalized BP estimation that couples a convolutional neural network CNN branch capturing global and local waveform dynamics with a morphology-prior branch that explicitly encodes person-specific vascular characteristics. By embedding a morphology-based feature set that explicitly encodes individual vascular characteristics, the proposed framework enhances personalization and reduces dependence on large-scale training datasets. Evaluated on a subset of the MIMIC-III database under a subject-specific personalized testing protocol, the proposed personalized physiology-guided hybrid approach achieved mean absolute errors MAEs of 3.77 mmHg for systolic BP and 2.36 mmHg for diastolic BP, corresponding to relative improvements of 43.7% and 32.4% over a subject-specific personalized CNN-only baseline. SHAP-based analysis confirmed that the introduced morphology-prior features align with individual vascular characteristics, reinforcing per-subject interpretability. These findings highlight the potential of personalized, physiology-guided hybrid learning with novel morphological descriptors for accurate and explainable BP monitoring in real-world settings.