An artificial neural network model as a tool to identify the anaerobic threshold during dynamic physical exercise

Anaerobic threshold is one of the most important parameters used in exercise physiology. It signals a power value during dynamic physical exercise where anaerobic energy formation for muscle contraction is added to the aerobic counterpart-what allows the quantification of aerobic capacity. In this study, we describe the development and validation of an artificial neural network model to identify anaerobic threshold based on electrocardiogram R-R interval time series collected during physical exercise tests applied in healthy subjects. The results showed that the artificial neural network had its best performance in gradual increasing power. Scatter plot and ROC curve was constructed showing high correlation (r = 0.93), and good accuracy (area under the ROC curve = 0.9851) when compared to autoregressive integrated moving average (ARIMA) statistical method.