## 3 Materials and Methods  
### Dataset  
We applied three criteria for selecting a database for RRI-based age prediction: (1) a recording length of at least 5 min, (2) a controlled measurement condition, and (3) a large number of samples. First, it is recommended to record at least 5 min-long RRI data to evaluate the characteristics of spectral components in studies on HRV [10]. A 5 min recording length is widely considered the gold standard for investigating short-term autonomic physiological responses [10]. Because the objective of this study was to build a deep learning model that detects age-related changes in autonomic regulation embedded in RRI sequences, we selected a database containing measurements that were at least 5 min in length. Second, we preferred measurements in a controlled laboratory environment during supine rest because subjects are less affected by external factors and their own activity, which facilitates the acquisition of stable RRIs. We did not use long-term ambulatory ECG recordings because of the lack of accurate information of the subject's status. Finally, we selected a large database of samples because a deep learning model typically requires a large number of samples for network training. In this study, we used a database called \"Autonomic Aging: a dataset to quantify changes of cardiovascular autonomic function during healthy aging,\" that was acquired from PhysioNet [14; 43]. It contains ECG recordings from 1121 healthy participants obtained at Jena University Hospital, Jena, Germany [14]. Participants with the following conditions were excluded from participating: any medical conditions, the use of illegal drugs or medication potentially influencing cardiovascular function, and any pathological findings from physical examination, resting ECG, and routine laboratory parameters [14]. The ECG signals were measured using the lead II configuration at a 1000 Hz sampling rate while participants were in a supine resting position [14]. The length of the recording was 8-35 min [14]. Individual age data from the participants were anonymized and generalized to the age groups. The database originally categorized ages into 15 five-year age groups, except for the youngest (18-19 years) and oldest (85-92 years) groups [14]. However, owing to a lack of samples in the elderly population and data imbalance, we reduced the number of total groups to four and combined subjects aged 50 and over into one group. Table 1 lists the number of participants in each age group used in this study. The dataset was highly imbalanced, with 64% of the total subjects in Group 1 (18-29 years). From a total of 1121 subjects, 1093 subjects were included in the final dataset after excluding 25 subjects because of lack of age information and 3 subjects because of low signal quality. Despite the class imbalance and lack of individual age data, the dataset used in this study is currently the best available option. The dataset contained more than 1000 ECG recordings with at least a 5 min length obtained from subjects in the supine resting position, \begin{table}
\begin{tabular}{l l l} \hline
**Age Group** & **Number of Subjects** & **Number of 5 min RRIs** \\ \hline Group 1 (18–29) & 696 (64\%) & 2280 (63\%) \\ Group 2 (30–39) & 147 (13\%) & 503 (14\%) \\ Group 3 (40–49) & 100 (9\%) & 365 (10\%) \\ Group 4 (\(\geq\)50) & 150 (14\%) & 458 (13\%) \\ \hline Total & 1093 (100\%) & 3606 (100\%) \\ \hline \end{tabular}
\end{table} 
Table 1: Number of subjects and 5 min RRI samples from each age group. satisfying criteria 1 and 2. Although PhysioNet has various ECG datasets with a higher number of samples, most of the recordings are extremely short in length or measured under ambulatory conditions, which does not fit the criteria [43]. Issues arising from data imbalance and the lack of age data are further discussed in the Discussion section.