Introduction: Electromyographic thresholds (EMGT) can provide individual muscle-specific feedback during incremental tests non-invasively. However, the determination of thresholds depends on signal processing methods [1] and often includes active as well as passive phases of movements, which might bias results. Therefore, the aim of this study was to compare visually and mathematically detected EMGT calculated from active movement phases only. Methods: Seven trained triathletes performed an incremental test on a cycle-ergometer. The respiratory compensation point (RCP) was determined using the VE versus VCO2 relationship [2]. EMG signal was measured continuously (3000 Hz) from vastus lat. (VL) and rectus fem. (RF) of both legs. In a computer-aided, semi-automatic step the activity intervals were registered manually and were used to assess the noise level. In a second step, the data were segmented into signal and pure-noise parts, followed by stabilizing the noise-signal-indicator [3]. Subsequently, several EMG parameters (integrated RMS, maxRMS, mean frequency) were calculated and time courses analyzed by 2 blinded peers to detect thresholds visually (EMGTv). Furthermore, EMG parameters were automatically fitted by minimizing the overall mean square error of three regression lines. A break between the second and third line was interpreted as the EMG threshold (EMGTa). Comparison of the methods was conducted by a repeated measures ANOVA and Pearson´s correlation coefficients. Results: Although several parameters showed a similar behavior, the integrated RMS appeared to be the most consistent signal to detect EMGT visually (EMGTv:100%). No significant difference was found between EMGTv, EMGTa and RCP (p>0.05) for both RF, and left VL. EMGTv and EMGTa did not differ significantly for right VL, but both were significantly different from RCP (p<0.05). Medium to high correlations were found between EMG thresholds and RCP (r=0.62-0.98). Discussion: The main outcome of this study was that muscular thresholds could be detected visually and automatically from the integrated RMS of active phases of the EMG signal in RF and VL during incremental tests. Comparisons to ventilatory thresholds revealed better agreement in RF than in VL. This could be explained by heterogeneity in muscle recruitment during incremental tests [4].
© Copyright 2016 21st Annual Congress of the European College of Sport Science (ECSS), Vienna, 6. -9. July 2016. Veröffentlicht von University of Vienna. Alle Rechte vorbehalten.