###### 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. biological age; age prediction; machine learning; deep learning; autonomic nervous system; RR intervals; heart rate variability; ResNet; class imbalance; ordinal classification Article  
Age Prediction in Healthy Subjects Using Heart Rate Variability: A Pilot Study Based on Deep Learning  
[PERSON], [PERSON], [PERSON]. The autonomic nervous system (ANS) regulates and maintains homeostasis in the cardiovascular system through the interaction between sympathetic and parasympathetic neural activity, particularly at the sinoatrial node [10; 11; 12]. The autonomic regulation of the cardiac system is affected by advancing age because of structural changes, such as the loss of sinoatrial pacemaker cells, and functional changes, which lead to gradual deterioration in the balanced interaction between the two branches of the ANS [13; 14]. This age-related change in autonomic cardiovascular control can be observed via the variation in cardiac beat-to-beat time intervals, known as heart rate variability (HRV) [12], which is quantified by evaluating the features of R-peak to R-peak intervals (RRIs) [10]. Heart rate variability features have been widely used to evaluate cardiac autonomic responsiveness to normal activities, external stimuli, and diseases [15; 16; 17]. Heart rate variability is considered a surrogate marker for cardiac autonomic regulation and the balance between sympathetic and vagal activities, although the underlying mechanism for the observed alterations in HRV is not fully understood [12]. Heart rate variability has been investigated as a non-invasive physiological biomarker to predict age because trends in HRV features associated with advancing age reflect age-induced variation in autonomic cardiac control in healthy subjects [18; 19; 20]. In particular, time, frequency, and nonlinear HRV features related to vagal modulation decrease with age in healthy subjects, indicating the prognostic significance of HRV indices in predicting age [18; 19; 20]. For example, [PERSON] et al. developed an equation that predicts the cardiac age of healthy subjects using frequency-domain HRV features [21]. Owing to the recent advances in machine learning, studies have applied various algorithms to HRV features to predict age or classify age groups [22; 23; 24; 25; 26]. For instance, [PERSON] et al. used neural networks for the regression analysis of HRV features to predict the age of 113 healthy subjects, resulting in a correlation coefficient of 0.872 between the predicted and true ages [22]. [PERSON] et al. used probabilistic neural network (PNN) algorithms to classify three age groups of 60 healthy men and achieved an accuracy of 70% [23]. [PERSON] et al.