Surface Electromyography Spectral Parameters for the Study of Muscle Fatigue in Swimming

Abstract

The purpose of this study was to assess validity, stability and sensitivity, of 4 spectral parameters–median frequency (Fmed), mean frequency (Fmean), Dimitrov index (DI), and mean instant frequency (Fmi)–in measuring localized muscle fatigue in swimming and to investigate their correlation with the variations of kinematic data and mechanical fatigue. Electrophysiological measures of muscle fatigue were obtained in real-time during a 100 m front crawl test at maximum speed in 15 experienced swimmers, using surface electromyography in six muscles employed in front crawl, while kinematic data of swimming was measured from video analysis. Mechanical fatigue was measured as the difference between muscle strength prior to and immediately after the 100 m front crawl in a dry-land multi-stage isometric contraction test. Statistically significant fatigue (p < 0.0001) was found for all spectral parameters in all muscles. Fmed and Fmean varied between 10 and 25%, DI between 50 and 150%, and Fmi between 5 and 10%. Strong correlation (Pearson r ≥ 0.5) with mechanical fatigue was found for all spectral parameters except for Fmi and it was strongest for Fmed and Fmean. From our study, it turns out that Fmed and Fmean are more valid and stable parameters to measure fatigue in swimming, while DI is more sensitive.

Changes in thrust force in swimmers in the annual training cycle

“…The time during which an athlete can maintain a maximum force in an exercise test is valuable information regarding the fatigue processes taking place in the skeletal muscles of the athlete [18]. In Other researchers studied neuromuscular fatigue and observed a failure to maintain the required or expected force, accompanied by changes in muscle activity [4,5] and also found the changes that occur in muscle activity over the 200 m breaststroke [7], which significantly differentiates the exercise over this distance from 50 m and 100 m [27,32]. Differences in the kinematics of breaststroke movements for 100 m and 200 m swimming were also demonstrated, confirming the differences that were observed during the measurements of thrust force in swimmers specializing in short distances (50 m and 100 m) and 200 m [37].…”

“…The participants included Olympic athletes and world, European, and Polish champions (Table 1). The results for individual athletes at the distances in which they specialized ranged from 896 to 958 FINA points (The level is based on the qualifying standards set for participation in international events that correspond to ≥ 875 FINA points) [27].…”

Electrophysiological and functional signs of Guillain–Barré syndrome predicted by a multiscale neuromuscular computational model

“…The PSD of the biceps brachii EMG signal in [72] has been shown to be useful to detect abnormalities related to neuropathies or myopathies at a diagnostic yield between 65% and 73% [72]. The PSD of the EMG signal has been particularly useful for the assessment of muscle fatigue [73][74][75][76], but also to investigate neuromuscular changes in stroke survivors [77,78], estimate muscle fibre composition [79], and as a support for the diagnosis of neuromuscular diseases such as Pompe disease [80] and Duchenne muscular dystrophy [81]. However, we have not found studies that use the EMG spectrum to support the diagnosis and follow-up of AIDP or other subtypes of GBS.…”

Muscle Fatigue and Swimming Efficiency in Behind and Lateral Drafting

“…During fatigue, because of the accumulation of catabolites such as inorganic phosphate and phosphocreatine, the acidity of the interstitial fluid increases causes a change in the shape of the action potential and a decrease of the muscle-fiber conduction velocity ( Brody et al, 1991 ). These physiological changes shift the EMG power spectrum to low frequencies so that the time evolution of the Mean Frequency of the EMG signal [MNF (Hz)] is a reliable parameter to estimate muscle fatigue ( Puce et al, 2021a ). The Root Mean Square of the EMG signal [RMS (μV)] is another parameter used in the analysis of muscle activity ( Del Vecchio et al, 2017 ).…”