ECG-derived Aerobic And Anaerobic Indices For Training Efficiency Tracking

Abstract

There is a continuous search for indirect methods and simple criteria to evaluate physiological effects of training. ECG analysis provides a relevant option for routine monitoring as it can be supported in real-time mobile or wearable device applications. Determination of the optimal ECG features is essential for monitoring and assessing systems. PURPOSE: To introduce ECG-derived aerobic index (AI) and anaerobic index (ANI) which could determine training effects and indicate subject’s metabolic state. METHODS: A healthy, physically active subject performed endurance and strength trainings 3 times a week. He fulfilled 55 ECG measurements using single-lead wrist-wearable device before and after 28 trainings. ECG signals were processed with detection of QRS-complex. AI and ANI were calculated as R-peak normalized to S-R complex slope and as S-T complex slope normalized to R-S slope. Correlations of AI and ANI with training load were calculated using Pearson correlation coefficient (r) with p value. RESULTS: Correlations between AI and aerobic load as well as ANI and anaerobic load were identified. The more energy was burned during training, the lower indices were registered. As shown in Figure, maximum of negative correlation between AI with aerobic load was in 60 min after training (r=-0.57, p<0.01). ANI showed negative correlation with anaerobic load (r=-0.35, p<0.01) in 30 min after training. CONCLUSION: Proposed ECG-derived aerobic and anaerobic indices showed statistically significant correlations with training load and could be used as assessed individual parameters of the degree of training in fitness and sport medicine. Figure. Dependences of ECG-derived aerobic (A) and anaerobic (B) indices on energy, burned during aerobic and anaerobic load. Correlation curves with confidence bounds (95 %).