The objective of the present study was to characterize the heart rate (HR) patterns of healthy males using the autoregressive integrated moving average (ARIMA) model over a power range assumed to correspond to the anaerobic threshold (AT) during discontinuous dynamic exercise tests (DDET). Nine young (22.3 ± 1.57 years) and 9 middle-aged (MA) volunteers (43.2 ± 3.53 years) performed three DDET on a cycle ergometer. Protocol I: DDET in steps with progressive power increases of 10 W; protocol II: DDET using the same power values as protocol 1, but applied randomly; protocol III: continuous dynamic exercise protocol with ventilatory and metabolic measurements (10 W/min ramp power), for the measurement of ventilatory AT. HR was recorded and stored beatto-beat during DDET, and analyzed using the ARIMA (protocols I and II). The DDET experiments showed that the median physical exercise workloads at which AT occurred were similar for protocols I and II, i.e., AT occurred between 75 W (116 bpm) and 85 W (116 bpm) for the young group and between 60 W (96 bpm) and 75 W (107 bpm) for group MA in protocols I and II, respectively; in two MA volunteers the ventilatory AT occurred at 90 W (108 bpm) and 95 W (111 bpm). This corresponded to the same power values of the positive trend in HR responses. The change in HR response using ARIMA models at submaximal dynamic exercise powers proved to be a promising approach for detecting AT in normal volunteers.

The aim of this study was to determine the anaerobic threshold (AT) in a population of healthy and post-myocardial infarction men by applying Hinkley's mathematical method and comparing its performance to the ventilatory visual method. This mathematical model, in lieu of observer-dependent visual determination, can produce more reliable results due to the uniformity of the procedure. 17 middle-aged men (55±3 years) were studied in 2 groups: 9 healthy men (54±2 years); and 8 men with previous myocardial infarction (57±3 years). All subjects underwent an incremental ramp exercise test until physical exhaustion. Breath-by-breath ventilatory variables, heart rate (HR), and vastus lateralis surface electromyography (sEMG) signal were collected throughout the test. Carbon dioxide output (V˙CO2), HR, and sEMG were studied, and the AT determination methods were compared using correlation coefficients and Bland-Altman plots. Parametric statistical tests were applied with significance level set at 5%. No significant differences were found in the HR, sEMG, and ventilatory variables at AT between the different methods, such as the intensity of effort relative to AT. Moreover, important concordance and significant correlations were observed between the methods. We concluded that the mathematical model was suitable for detecting the AT in both healthy and myocardial infarction subjects.