## 2 Related Works  
Estimating age based on facial images is widely recognized as the most prevalent non-invasive technique for age prediction [39]. Previous studies have demonstrated strong performance in age prediction using conventional machine learning or deep learning algorithms [39]. One of the major benefits of this method is its accessibility because obtaining facial images does not require expertise. However, cosmetics and facial expressions can affect estimation based on facial images, impairing the accuracy of such predictions [39]. By contrast, using physiological signals regulated by the ANS for age prediction presents an advantage because these signals are not affected by such external factors and are resistant to deception. However, acquiring physiological signals requires training and specific equipment, making it less accessible than facial imaging. Non-invasive methods to evaluate biological age using physiological signals have been demonstrated in previous studies. For example, deep learning-based age prediction was performed using an electrocardiogram (ECG), which is one of the physiological signals that can be easily obtained [8; 9]. In these works, 10 s-long 12-lead ECG signals were used as input data, and a CNN model was implemented to estimate age as a continuous variable. [PERSON] et al. achieved a 0.7 \(r\)-squared between estimated and chronological ages and found that patients with an estimated age having a deviation of more than seven years from chronological age exhibited a higher incidence of comorbidities [9]. These studies trained deep learning models to learn raw ECG waveforms with a very short length (~10 s) instead of RRIs. Although 10 s-long ECG signals were long enough to reflect cardiac aging, it is not clear whether the same signals can be used to represent autonomic physiological responses due to their short length [40]. Because our study was aimed at characterizing aging in terms of ANS using RRIs, the rest of this section focuses on reviewing previous works based on RRIs. Recently, an increasing number of studies have implemented deep learning methods without relying on feature engineering for the analysis of RRI time-series data. One of the major contributions of these studies was the automated detection of altered heartbeat rhythms caused by different types of arrhythmias [29; 30; 31; 32; 33; 34]. The methods introduced were based on the observation that the distribution of RRIs during arrhythmias differs from that during NSRs [41]. For this purpose, the RRI sequence was extracted from ECG recordings using beat annotations manually labeled by expert clinicians, or by applying an R-peak detection algorithm to raw ECG signals when manual annotations were not available. In some cases, RRI data were split into shorter segments, particularly when ECG measurements were based on a long-term monitoring system such as Holter. Subsequently, the RRI data were input to various DNN models to classify two to six different types of heartbeat rhythms, including the NSR as a healthy control. For example, [PERSON] et al. used 100-beat RRI sequences with an LSTM model to distinguish between AFIB and NSR [29]. [PERSON] et al. employed 30-beat RRIs with a deep learning model combining a CNN and LSTM to identify AFIB [30]. [PERSON] et al. proposed a DNN model consisting of a CNN and LSTM using 30 s RRIs to classify AFIB, atrial flutter, and NSR [31]. [PERSON] et al. detected AFIB in 10 s RRIs extracted from patch-based ECG devices using a CNN model [32]. [PERSON] et al. used 360 s RRI data with a CNN model to detect ventricular tachyarrhythmia; their model outperformed classical machine learning methods with HRV features [33]. [PERSON] and [PERSON] implemented a ResNet model with 10 s RRI to classify six different beat types [34]. These studies achieved excellent performance in classifying beat types, with approximately 85-99% accuracy. In addition, RRIs have been used to detect sleep apnea based on deep learning.