Abstract
Abstract
This study aimed to investigate whether explainable features extracted from standard 12-lead electrocardiography (ECG) can be used to identify older adults and assess ECG-derived cardiac aging patterns, and to implement the resulting model as a lightweight web-based interpretation prototype. A total of 21,373 ECG records from individuals aged 18–89 years were included from the publicly available PTB-XL dataset. Lead-specific statistical features related to amplitude, variability, energy, waveform change, and zero-crossing patterns were extracted from 10-s, 100-Hz, 12-lead ECG signals in records100. Adults aged 65 years or older were defined as the older-age group. Logistic regression, random forest, and XGBoost models were compared. XGBoost achieved moderate discrimination for older-age classification, with an AUROC of 0.805 in the PTB-XL strat_fold-based test set and a Brier score of 0.181. In five-fold patient-level grouped validation, the model achieved a mean AUROC of 0.797 ± 0.007 with no patient overlap between training and test folds. In age regression, XGBoost achieved a mean absolute error of 10.31 years. The age-dependent bias of the raw cardiac age gap was removed using regression-based correction. The accelerated cardiac aging group, defined as the upper quartile of the corrected cardiac age gap, showed a lower prevalence of normal ECG findings and higher prevalence of ECG abnormalities, MI-related patterns, ST-T changes, conduction disturbances, and hypertrophy than the younger cardiac pattern group. These findings suggest that explainable ECG-derived features may support exploratory cardiac aging pattern assessment and provide a basis for research-oriented web-based interpretation tools. However, any broader applicability of this framework remains speculative, and the prototype should not be considered ready for clinical deployment, population screening, or general use without independent external validation.