title: Real-Time Classification of Exercise Exertion Levels Using Discriminant Analysis of HRV Data.
abstract: Heart rate variability (HRV) was shown to reflect activation of sympathetic nervous system however it is not clear which set of HRV parameters is optimal for real-time classification of exercise exertion levels. There is no studies that compared potential of two types of HRV parameters (time-domain and frequency-domain) in predicting exercise exertion level using discriminant analysis. The main goal of this study was to compare potential of HRV time-domain parameters versus HRV frequency-domain parameters in classifying exercise exertion level. Rest, exercise, and recovery categories were used in classification models. Overall 79.5% classification agreement by the time-domain parameters as compared to overall 52.8% classification agreement by frequency-domain parameters demonstrated that the time-domain parameters had higher potential in classifying exercise exertion levels.
authors.affiliation: Johns Hopkins University, Baltimore, MD, USA.
authors.forename: In Cheol
authors.initials: IC
authors.lastname: Jeong
authors.affiliation: Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Joseph
authors.initials: J
authors.lastname: Finkelstein
mesh_terms: D016002:Discriminant Analysis; D015444:Exercise / Q000502:physiology*; D005080:Exercise Test; D006339:Heart Rate / Q000502:physiology*; D006801:Humans; D005082:Physical Exertion / Q000502:physiology*