Accuracy of neuro-fuzzy logic and regression calculations in determining maximal lactate steady-state power output from incremental tests in humans

Abstract

The aim of this study was to employ neuro-fuzzy logic and regression calculations to determine the accuracy of prediction of the power output ( P) of the maximal lactate steady-state (MLSS) on a cycle ergometer calculated from the results of incremental tests. A group of 17 male and 17 female sports students underwent two incremental tests (a 1 min test T(1): initial exercise intensity 0.2 W x kg(-1) increasing 0.2 W x kg(-1) every minute; a 3 min test T(3): initial exercise intensity 0.6 W x kg(-1) increasing 0.6 W x kg(-1) every 3 min) and at least four constant-intensity tests of 30 min duration. Two models for MLSS calculation were developed using the data from T(1) and T(3), a forward stepwise linear regression model (REG) and a neuro-fuzzy model (FUZ). A group of 26 randomly selected subjects (model group, MG) were used to generate the REG and the FUZ models. The data from the remaining 8 subjects (4 men and 4 women; verifying group, VG) were used to verify the REG and FUZ models. The precision of the MLSS calculation in MG produced a better correlation when using data from T(1) (REG r=0.95, FUZ r=0.99) than data from T(3) (REG r=0.88, FUZ r=0.98). Our calculation models were confirmed using data from VG for T(1) (REG r=0.97, FUZ r=0.98) as well as for T(3) (REG r=0.97, FUZ r=0.97). Based on our subject population of young, healthy sport students, our results suggest that a single incremental test may be used for prediction of P at the MLSS using a cycle ergometer. Furthermore, the results from T(1) yielded higher correlations compared to T(3). Calculations from REG were similar to FUZ but the precision of REG and FUZ was better compared to calculations derived using data from a single threshold.

Blood lactate concentration at the maximal lactate steady state is not dependent on endurance capacity in healthy recreationally trained individuals

“…The highly significant correlation between MLSS power output and power output at RCP and LTP 2 (Fig. 1a, b) as well as maximum power output (P \ 0.001) are in agreement with previous results published by our group (Smekal et al 2002). However, higher values were found for power output at the RCP (?11.6.0%) and LTP 2 (?8%) compared to power output at MLSS.…”

The midpoint between ventilatory thresholds approaches maximal lactate steady state intensity in amateur cyclists

“…The role of MLSS as an index of aerobic endurance [4, 18, 44–46] and as a training stimulus to improve this ability [10, 47, 48] has motivated the search for a single assessment protocol [45, 49–51], since the gold standard protocol comprises the performance of an incremental test followed by successive constant intensity tests [5, 9, 52]. Although some of the defined points during an incremental test have been proposed as intensities that indicate MLSS, which would permit its determination with one single test [1, 15, 19, 25, 27, 28, 52–55], none of these studies is definitive, and the challenge remains to be able to determine this intensity with just one test.…”

Evaluation of 12 antibodies for distinguishing epithelioid mesothelioma from adenocarcinoma: identification of a three-antibody immunohistochemical panel with maximal sensitivity and specificity

“…37 An adaptive binary regression method, logic regression (LR), 38,39 was chosen over classical logistic regression because LR is more appropriate for combining binary predictor [38][39][40][41][42] LR methodology, which has been previously used for combining biomarkers [41][42][43][44] produces 'and' and 'or' (Boolean) classification rules to identify combinations of variables best able to predict a certain outcome, and are easily interpreted by clinicians. In this study, we sought the combination of antibodies best suited to distinguish MEM from AdCA.…”

“…In conclusion, our study goes beyond most previous publications in this area, in the following ways: (a) we evaluated a particularly large number of markers, for which the immunohistochemical techniques were previously screened and optimized; (b) we evaluated a very large set of MEM and AdCA from numerous geographic locations; and (c) we used a variety of robust statistical methods [38][39][40][41][42] to design a cost-effective panel of only three monoclonal antibodies, without compromising sensitivity or specificity.…”