Elevated ECG age relative to chronological age is associated with atherosclerotic cardiovascular disease, abnormal peripheral endothelial dysfunction, and higher cardiovascular mortality.
Can machine learning and deep learning models accurately estimate age and gender from 12-lead ECG signals, and does the estimated age correlate with cardiovascular risk?
Machine learning-derived ECG age serves as a physiological biomarker, where an elevated ECG age relative to chronological age indicates an aged cardiovascular system and higher mortality risk.
Twelve lead electrocardiogram signals capture unique fingerprints about the body's biological processes and electrical activity of heart muscles. Machine learning and deep learning-based models can learn the embedded patterns in the electrocardiogram to estimate complex metrics such as age and gender that depend on multiple aspects of human physiology. ECG estimated age with respect to the chronological age reflects the overall well-being of the cardiovascular system, with significant positive deviations indicating an aged cardiovascular system and a higher likelihood of cardiovascular mortality. Several conventional, machine learning, and deep learning-based methods have been proposed to estimate age from electronic health records, health surveys, and ECG data. This manuscript comprehensively reviews the methodologies proposed for ECG-based age and gender estimation over the last decade. Specifically, the review highlights that elevated ECG age is associated with atherosclerotic cardiovascular disease, abnormal peripheral endothelial dysfunction, and high mortality, among many other cardiovascular disorders. Furthermore, the survey presents overarching observations and insights across methods for age and gender estimation. This paper also presents several essential methodological improvements and clinical applications of ECG-estimated age and gender to encourage further improvements of the state-of-the-art methodologies.
Ansari et al. (2023) conducted a review in Cardiovascular disorders. ECG-estimated age vs. Chronological age was evaluated. Elevated ECG age relative to chronological age is associated with atherosclerotic cardiovascular disease, abnormal peripheral endothelial dysfunction, and higher cardiovascular mortality.