Improving age prediction using ECG signals: Insights into lifestyle impacts

BACKGROUND: The discrepancy between cardiac age, estimated from electrocardiograms (ECGs), and chronological age can serve as an indicator of cardiovascular health. A significant difference may reflect vascular aging and elevated risk, but improving the precision and interpretability of these predictions remains challenging. METHODS: This study proposes novel preprocessing techniques to improve prediction accuracy and investigates the relationship between lifestyle factors and cardiac aging. ECG signals were processed using a Butterworth bandpass filter combined with a Hyperbolic Tangent transformation. A loss-tolerant training mechanism was introduced to reduce the impact of label noise. The model was trained on the PTB-XL dataset, and lifestyle-related analyses were conducted using the PLHDB and AISDB databases, which include both ECG recordings and behavioural information. RESULTS: The proposed model achieved a mean absolute error (MAE) of 6.76 years on the PTB-XL dataset, outperforming existing approaches under comparable conditions. Lifestyle analysis revealed that regular exercise was associated with a 1.31-year reduction in predicted cardiac age, while smoking and excessive alcohol intake were associated with increases of 3.67 and 1.22 years, respectively. CONCLUSIONS: These findings demonstrate the utility of ECG-based deep learning models not only for precise cardiac age estimation but also for elucidating the effects of lifestyle behaviours on cardiovascular health.