AI-based prediction of VO₂ max from 24-h Holter ECG recording

Cardiovascular (CV) deconditioning is a consequence of spaceflight, characterized by functional and structural changes in the heart and blood vessels due to prolonged exposure to microgravity. These adaptations lead to a noticeable reduction in exercise capacity, particularly evident in alterations of maximum aerobic power (VO₂ max [mL/kg/min]). This work explored the hypothesis that microgravity-induced CV deconditioning could be anticipated through information derived from a non-exercise longitudinal 24-h Holter ECG recording, thereby offering a means for its regular monitoring in space. Pooled data acquired at baseline and at the last day of six Head-Down Tilt bed rest campaigns of different duration were utilized to test this hypothesis. Machine Learning models were developed using multiple features to predict VO₂ max after deconditioning. Obtained results showed good prediction accuracy (RMSE < 5 mL/kg/min) demonstrating the potential of this approach to identify VO₂ max deterioration during bed rest, with possible applications in real world space environments and clinical settings.