BioAgeNet: An Age-Informed Convolutional Autoencoder for ECG Clustering Indicating Health

Biological Age (BA) indicates the authentic ageing progression of an individual in relation to their quality of life. The noninvasive identification of BA is crucial in predicting longevity and early age-related diseases and enabling personalized healthcare. Potential biomarkers of BA are vague and need attention. The ageing process stands out as a prominent risk factor for cardiovascular diseases. Consequently, an Electrocardiogram (ECG), the most popular and easily accessible signal, is explored to analyze the effect of age. Numerous studies have delved into supervised deep-learning approaches for ECG analysis, particularly in predicting age. These studies rely on regression-based methods and necessitate additional analysis for extracting health-related insights, such as the correlation of error between Chronological Age and AI-predicted Age with mortality. Moreover, as the shortage of cardiologists' annotated data is apparent, we propose an Age-Informed Convolutional Autoencoder that clusters ECG deep features associated with age to assess the quality of life possessed at the current age. We also proposed a three-step training strategy combining model training and deep ECG features clustering with a controlled initialization. We find that a combination of age and ECG reveals the heart's BA and is a contributing biomarker for estimating the overall BA of the body. This approach marks substantial progress in analyzing age-related impacts on ECG. It provides new perspectives on different cardiovascular disorders and can potentially transform personalized healthcare in the future.