XVPE-Net: A Novel Methodology for Interpretable Vital Parameter and Cuffless Blood Pressure Estimation from PPG Signal

The interpretability of machine learning model is crucial in healthcare as it fosters reliability and supports clinicians in making informed decisions based on predictions. However, recent approaches for vital parameter estimation often act as black boxes, lacking clarity in their predictive reasoning. Therefore, accurate estimation of vital parameters is crucial not only for timely diagnosis and effective patient monitoring but also for ensuring that clinicians can trust and understand the model’s predictions. In this study, we introduce XVPE-Net, an explainable framework aimed at improving the state-of-the-art VPE-Net model for estimating vital parameters: heart rate (HR), respiratory rate (RR), systolic blood pressure (SBP), and diastolic blood pressure (DBP). Our model enhances interpretability by visually highlighting key parts of the PPG signal that significantly influence VPE-Net’s predictions of vital parameters. We test our model using 1,000 PPG segments from 38 subjects in the MIMIC-III dataset, which is available in the Physionet repository. The results show strong predictive capabilities, along with better transparency and reliability. This study helps us understand how the model makes decisions and supports the use of explainable AI in health monitoring, contributing to the development of more reliable models for medical applications.