Comparative utility of time and frequency HRV domains for automated classification of exercise exertion levels

Abstract

Exercise exertion results in activation of sympathetic nervous system. Heart rate variability (HRV) has been used to analyze activity of sympathetic nervous system (ANS). However, approaches to use HRV for exercise exertion analysis were not explored systematically. The main goal of this study was to develop classification algorithms to determine level of exercise exertion in real time and to compare potential of HRV time domain parameters versus HRV frequency domain parameters versus combined time and frequency parameter set. Discriminant analysis was used to identify optimal parameter sets and to develop algorithms for classification of exercise exertion levels. Time-domain HRV parameters demonstrated higher classification accuracy (95.6%) as compared to frequency-domain parameters (82.2%). Combing HRV parameters from time and frequency domains improves classification accuracy (97.8%). Our results suggested that HRV analysis can be used to automatically classify exercise exertion levels. Future studies should focus on more granular approach in identifying different stages of exercise process. Evaluation of classification algorithms should be based on larger sample of diverse representatives of different age, sex and health condition groups.