Abstract

Background: The second heart rate (HR) turn point has been extensively studied, however there are few studies determining the first HR turn point. Also, the use of mathematical and statistical models for determining changes in dynamic characteristics of physiological variables during an incremental cardiopulmonary test has been suggested. Objectives: To determine the first turn point by analysis of HR, surface electromyography (sEMG), and carbon dioxide output ((V)Over dotCO(2)) using two mathematical models and to compare the results to those of the visual method. Method: Ten sedentary middle-aged men (53.9 +/- 3.2 years old) were submitted to cardiopulmonary exercise testing on an electromagnetic cycle ergometer until exhaustion. Ventilatory variables, HR, and sEMG of the vastus lateralis were obtained in real time. Three methods were used to determine the first turn point: 1) visual analysis based on loss of parallelism between (V)Over dotCO(2) and oxygen uptake ((V)Over dotO(2)); 2) the linear-linear model, based on fitting the curves to the set of (V)Over dotCO(2) data (Lin-Lin(V)Over dotCO(2)); 3) a bi-segmental linear regression of Hinkley's algorithm applied to HR (HMM-HR), (V)Over dotCO(2) (HMM-(V)Over dotCO(2)), and sEMG data (HMM-RMS). Results: There were no differences between workload, HR, and ventilatory variable values at the first ventilatory turn point as determined by the five studied parameters (p>0.05). The Bland-Altman plot showed an even distribution of the visual analysis method with Lin-Lin(V)Over dotCO(2), HMM-HR, HMM-(V)Over dotCO(2), and HMM-RMS. Conclusion: The proposed mathematical models were effective in determining the first turn point since they detected the linear pattern change and the deflection point of (V)Over dotCO(2), HR responses, and sEMG.