Wavelet and principal component analysis of electromyographic activity and slow component of oxygen uptake during heavy and severe cycling exercise

Abstract

The aim of the study was to investigate whether the slow component of oxygen uptake was concurrent with the recruitment of large α-motoneuron muscle fibres by using wavelet and principal component analysis (PCA) of electromyography (EMG) during heavy and severe cycling exercise. Eleven male subjects participated in the study. After establishing each subject’s maximum value of oxygen uptake through an incremental test on the cycle ergometer, the subjects performed 6-min cycling tests at heavy and severe intensity. EMG signals were collected from rectus femoris, biceps femoris long head, tibialis anterior, and medial gastrocnemius and processed by combined use of wavelet and PCA analysis. The time delays to the onset of slow component occurred significantly earlier during severe (105.22 ± 5.45 s) compared with during heavy (138.78 ± 15.09 s) exercise. ANOVA with repeated measures showed that for all muscles tested, the angle θ formed by the first and second principal components decreased significantly between time windows during heavy and severe exercise. However, significant increases of EMG mean power frequency (MPF) were found only during heavy exercise. Our results show the concurrence of the oxygen uptake slow component with the additional recruitment of muscle fibres, presumably less efficient large α-motoneuron fibres. Novelty The expected rise in MPF may be offset by muscle fatigue occurring in the later time windows of the slow component during severe exercise. The gradual shift to higher EMG frequencies throughout the slow-component phase was reflected in the progressive and significant decrease of angle θ.

How Priming Exercise Affects Oxygen Uptake Kinetics: From Underpinning Mechanisms to Endurance Performance

“…Furthermore, the recruitment of additional higher order motor units during heavy exercise occurs in close temporal association with the development of the O 2 slow component [ 37 , 138 ]. Finally, the increase of the iEMG signal throughout heavy exercise has been demonstrated to correlate closely with the O 2 slow component amplitude [ 139 , 140 ]. In summary, single muscle fibre studies evincing changes in following priming likely rule out a role for motor unit recruitment in determining changes in this parameter.…”

Application of Surface Electromyography in Exercise Fatigue: A Review

“…Dimension reduction plays a crucial role in index extraction, which can reduce the calculation time. Common dimension reduction methods include PCA ( Qi et al, 2020 ) and ICA ( Too et al, 2018a ).…”

“…Dimension reduction plays a crucial role in index extraction, which can reduce the calculation time. Common dimension reduction methods include PCA (Qi et al, 2020) and ICA (Too et al, 2018a). Khan et al (2019) used the random forest trained by distributive power frequency of the sEMG signal of the vastuslateralis muscle to predict muscle fatigue.…”