A Hybrid Windkessel-Neural Approach for Improved Noninvasive Blood Pressure Monitoring

TL;DR

This paper introduces a hybrid model combining Windkessel physics with neural networks to enhance cuffless blood pressure estimation, improving interpretability and robustness.

Contribution

It reformulates Windkessel into a neural network-compatible form, integrating physical constraints into machine learning for better physiological validity.

Findings

Improved BP estimation accuracy on MIMIC-II dataset

Enhanced model interpretability and robustness

Physical constraints lead to more physiologically valid predictions

Abstract

Owing to the recent advancements in wearable devices for health care, the importance of BP estimation without cuffs increases. Cuff technologies are inappropriate for continuous BP measurement due to their inconvenient usage, invasive character, necessity of calibration, large size, and inability to perform long-term monitoring. Normally, the algorithm used for cuffless BP prediction employs machine learning models that operate according to the data-driven approach. However, although they show high numerical accuracy, ML models do not provide any interpretability, resulting in poor physiological validity and clinical applicability. We propose a combination of Windkessel and ML models that incorporates the physical aspects into the latter one. It is performed by reformulating Windkessel into a form that will allow employing ML models. The result is a system of ODEs which can be used in the neural network. Thus, the inclusion of physical constraints improves the data-driven approach by making models consistent with physics, understandable, and robust. For illustration, we apply the described technique using a publicly available MIMIC-II database that we obtain from the UCI Machine Learning Repository.