Identification and agreement of first turn point by mathematical analysis applied to heart rate, carbon dioxide output and electromyography

Abstract

BackgroundThe 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. ObjectivesTo determine the first turn point by analysis of HR, surface electromyography (sEMG), and carbon dioxide output () using two mathematical models and to compare the results to those of the visual method. MethodTen 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 and oxygen uptake (); 2) the linear-linear model, based on fitting the curves to the set of data (Lin-Lin ); 3) a bi-segmental linear regression of Hinkley' s algorithm applied to HR (HMM-HR), (HMM- ), and sEMG data (HMM-RMS). ResultsThere 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 , HMM-HR, HMM-CO2, and HMM-RMS. ConclusionThe proposed mathematical models were effective in determining the first turn point since they detected the linear pattern change and the deflection point of , HR responses, and sEMG.

Apolipoprotein B and angiotensin-converting enzyme polymorphisms and aerobic interval training: randomized controlled trial in coronary artery disease patients

“…VAT was determined from the loss of parallelism between oxygen uptake (VO 2 ) and carbon dioxide production (VCO 2 ) by three properly trained observers, as previously described by Zamunér et al ( 20 ) and Higa et al ( 21 ).…”

Interval training based on ventilatory anaerobic threshold increases cardiac vagal modulation and decreases high-sensitivity c-reative protein: randomized clinical trial in coronary artery disease

“…The VAT was determined from a loss of parallelism between VO 2 (oxygen consumption) and VCO 2 (carbon dioxide output) by three properly trained observers, as previously described by Zamunér et al 16 and Higa et al 17 .…”

Anaerobic Threshold by Mathematical Model in Healthy and Post-Myocardial Infarction Men

“…The reason for the investigation of alternative methods is the intention to apply models that can be used in physical training and rehabilitation environments. The application of mathematical models in more accessible variables is a low-cost alternative and can produce reliable results [3,9,25,27]. Conventional protocols are designed to adequately determine AT in athletes and healthy individuals.…”

“…We concluded that the mathematical model was suitable for detecting the AT in both healthy and myocardial infarction subjects. that the mathematical models were sensitive in the detection of AT, these studies were performed in the vast majority with healthy individuals [3,9,25,27], and to our knowledge, there have not been any studies to date that have applied mathematical models to determine the AT in cardiac patients. We hypothesized that individuals with previous myocardial infarction (MI) have a breaking point in the cardiopulmonary and muscle variables during the exercise protocol and that this point corresponds to the AT, similar to healthy individuals.…”