Physiological-model-based neural network for modeling the metabolic-heart rate relationship during physical activities

TL;DR

This paper presents a novel physiological-model-based neural network that accurately estimates heart rate from oxygen uptake data, improving personalized cardiac monitoring during daily activities by integrating physiological constraints into the neural network.

Contribution

The study introduces a new neural network framework that embeds physiological constraints for improved heart rate estimation, combining physiological modeling with deep learning for personalized health monitoring.

Findings

Achieved median R² of 0.8 and RMSE of 8.3 bpm in HR estimation.

Performed on par with benchmark neural network models, outperforming traditional physiological models.

Enabled personalized parameter identification for physiological modeling.

Abstract

Heart failure (HF) poses a significant global health challenge, with early detection offering opportunities for improved outcomes. Abnormalities in heart rate (HR), particularly during daily activities, may serve as early indicators of HF risk. However, existing HR monitoring tools for HF detection are limited by their reliability on population-based averages. The estimation of individualized HR serves as a dynamic digital twin, enabling precise tracking of cardiac health biomarkers. Current HR estimation methods, categorized into physiologically-driven and purely data-driven models, struggle with efficiency and interpretability. This study introduces a novel physiological-model-based neural network (PMB-NN) framework for HR estimation based on oxygen uptake (VO2) data during daily physical activities. The framework was trained and tested on individual datasets from 12 participants engaged in activities including resting, cycling, and running. By embedding physiological constraints, which were derived from our proposed simplified human movement physiological model (PM), into the neural network training process, the PMB-NN model adheres to human physiological principles while achieving high estimation accuracy, with a median R$^2$ score of 0.8 and an RMSE of 8.3 bpm. Comparative statistical analysis demonstrates that the PMB-NN achieves performance on par with the benchmark neural network model while significantly outperforming traditional physiological model (p=0.002). In addition, our PMB-NN is adept at identifying personalized parameters of the PM, enabling the PM to generate reasonable HR estimation. The proposed framework with a precise VO2 estimation system derived from body movements enables the future possibilities of personalized and real-time cardiac monitoring during daily life physical activities.