Fractals and Electromyograms

Arjunan, Sridhar P.; Kumar, Dinesh

doi:10.1007/978-1-4939-3995-4_27

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…The FD is a parameter used to detect the characteristics of the mechanisms generating a signal. Therefore, it is used to extract the characteristic features of physiological signals including EMG signals [32,33,[49][50][51][52][53]. The FD analysis method has been utilized previously to study the dynamics of various muscles, including the biceps brachii [20,49,50], vastus lateralis [20,28], vastus medalis [28,31,35], rectus femoris [24,31,35], and flexor and extensor carpi radialis muscles [52].…”

Section: Discussionmentioning

confidence: 99%

“…Also, the FD of an EMG signal changes depending on the type of contraction, the task that the muscle performs and whether there is a disorder in the musculoskeletal system [24][25][26][27]. Therefore, FD analysis methods are frequently used in addition to time-domain analysis methods to identify how the dynamics of the musculotendon system change following an intervention, to understand the role of central and muscle-related mechanisms behind these changes and to examine whether the FD could be used as a discriminating parameter [28][29][30][31][32][33].…”

Section: Introductionmentioning

confidence: 99%

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Effects of Transverse Friction Massage on the Electromechanical Delay Components and Fractal Dimension of Surface Electromyography in Quadriceps Muscles

et al. 2023

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The purpose of this study was to assess the effects of transverse friction massage (TFM) on the electromechanical delay components and complexity of the surface electromechanical activity in the rectus femoris (RF) and vastus medialis (VM) muscles and to identify possible mechanisms behind TFM-induced alterations in the dynamics of RF and VM activity. Seven female and five male healthy subjects participated in this study. The subjects generated five maximal voluntary isometric contractions (MVICs) consecutively before and after TFM. Meanwhile, electromyography (EMG), mechanomyography (MMG), and force were recorded. The onset times of the recorded signals were detected offline by setting the threshold to three times the SD of the baseline. The delays between EMG and MMG (Δt(EMG–MMG)), MMG and force (Δt(MMG–Force)), and EMG and force (Δt(EMG–Force)) were computed from the detected onsets. The fractal dimension (FD) of the EMG time series was computed using the correlation dimension method. TFM increased Δt(MMG–Force) and Δt(EMG–Force) significantly in the RF but decreased Δt(EMG–MMG) and increased Δt(MMG–Force) in the VM. TFM decreased the FD in the RF and increased it in the VM. The results imply that TFM decreased the stiffness of both the RF and VM and decreased the duration of the electrochemical processes in the VM. It is proposed that the decrease in EMG complexity in the RF may be associated with the decreased stiffness of the RF, and the increase in EMG complexity in the VM may be associated with the decreased electrochemical processes in this muscle. It is also suggested that the opposite changes in EMG complexity in the RF and VM can be used as a discriminating parameter to search for the effects of an intervention in the quadriceps muscles. The present study also demonstrates how to discriminate the nonlinear dynamics of a complex muscle system from a noisy time series.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effects of Transverse Friction Massage on the Electromechanical Delay Components and Fractal Dimension of Surface Electromyography in Quadriceps Muscles

et al. 2023

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Increased resistance towards fatigability in patients with facioscapulohumeral muscular dystrophy

et al. 2021

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Purpose In facioscapulohumeral muscular dystrophy (FSHD) fatigue is a major complaint. We aimed to investigate whether during isometric sustained elbow flexions, performance fatigability indexes differ in patients with FSHD with respect to healthy controls. Methods Seventeen patients with FSHD and seventeen healthy controls performed two isometric flexions of the dominant biceps brachii at 20% of their maximal voluntary contraction (MVC) for 2 min and then at 60% MVC until exhaustion. Muscle weakness was characterized as a percentage of predicted values. Maximal voluntary strength, endurance time and performance fatigability indices (mean frequency of the power spectrum (MNF), muscle fiber conduction velocity (CV) and fractal dimension (FD)), extracted from the surface electromyogram signal (sEMG) were compared between the two groups. Results In patients with FSHD, maximal voluntary strength was 68.7% of predicted value (p < 0.01). Compared to healthy controls, FSHD patients showed reduced MVC (p < 0.001; r = 0.62) and lower levels of performance fatigability, characterized by reduced rate of changes in MNF (p < 0.01; r = 0.56), CV (p < 0.05; 0.37) and FD (p < 0.001; r = 0.51) and increased endurance time (p < 0.001; r = 0.63), during the isometric contraction at 60% MVC. Conclusion A decreased reduction in the slopes of all the considered sEMG parameters during sustained isometric elbow flexions suggests that patients with FSHD experience lower levels of performance fatigability compared to healthy controls.

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Measuring complexity in different muscles during sustained contraction using fractal properties of SEMG signal

Arjunan

Kumar

2018

2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Fractals and Electromyograms

Cited by 3 publications

References 30 publications

Effects of Transverse Friction Massage on the Electromechanical Delay Components and Fractal Dimension of Surface Electromyography in Quadriceps Muscles

Effects of Transverse Friction Massage on the Electromechanical Delay Components and Fractal Dimension of Surface Electromyography in Quadriceps Muscles

Increased resistance towards fatigability in patients with facioscapulohumeral muscular dystrophy

Measuring complexity in different muscles during sustained contraction using fractal properties of SEMG signal

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