Age Prediction in Healthy Subjects Using RR Intervals and Heart Rate Variability: A Pilot Study Based on Deep Learning

Abstract

Autonomic cardiac regulation is affected by advancing age and can be observed by variations in R-peak to R-peak intervals (RRIs). Heart rate variability (HRV) has been investigated as a physiological marker for predicting age using machine learning. However, deep learning-based age prediction has rarely been performed using RRI data. In this study, age prediction was demonstrated in a healthy population based on RRIs using deep learning. The RRI data were extracted from 1093 healthy subjects and applied to a modified ResNet model to classify four age groups. The HRV features were evaluated using this RRI dataset to establish an HRV-based prediction model as a benchmark. In addition, an age prediction model was developed that combines RRI and HRV data. The adaptive synthetic algorithm was used because of class imbalance and a hybrid loss function that combined classification loss and mean squared error functions was implemented. Comparisons suggest that the RRI model can perform similarly to the HRV and combined models, demonstrating the potential of the RRI-based deep learning model for automated age prediction. However, these models showed limited efficacy in predicting all age groups, indicating the need for significant improvement before they can be considered reliable age prediction methods.

Keywords

• biological age
• age prediction
• machine learning
• deep learning
• autonomic nervous system
• RR intervals