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

Area of Science:

• Biomedical Engineering
• Cardiology
• Gerontology

Background:

• Biological Age (BA) is a critical indicator of aging and healthspan, yet reliable noninvasive biomarkers are lacking.
• Aging is a major risk factor for cardiovascular diseases, highlighting the need for age-related cardiovascular assessments.
• Current deep learning methods for ECG-based age prediction often require additional analysis for health insights.

Purpose of the Study:

• To develop a novel, noninvasive method for assessing Biological Age (BA) using Electrocardiograms (ECGs).
• To explore the utility of ECGs as a biomarker for age-related cardiovascular health and overall quality of life.
• To address data limitations in cardiologist-annotated ECGs by proposing an unsupervised deep learning approach.

Main Methods:

• An Age-Informed Convolutional Autoencoder was developed to cluster deep ECG features associated with age.
• A three-step training strategy was employed, integrating model training, feature clustering, and controlled initialization.
• The study analyzed the combined predictive power of chronological age and ECG data for assessing BA.

Main Results:

• The combination of chronological age and ECG data effectively reveals the heart's Biological Age (BA).
• ECG-derived BA serves as a significant contributing biomarker for estimating the body's overall BA.
• The proposed method demonstrates substantial progress in analyzing age-related ECG changes and offers new insights into cardiovascular health.

Conclusions:

• Noninvasive assessment of BA using ECGs is feasible and holds promise for personalized healthcare.
• ECG analysis, particularly with advanced deep learning, can provide valuable information beyond chronological age.
• This approach offers a new perspective on cardiovascular disorders and aging, potentially transforming personalized medicine.