Fractal Dimension Estimation Via Spectral Distribution Function and Its Application to Physiological Signals

Chang, Shyang; Li, Shiun-Jeng; Chiang, Meng-Ju; Hu, Shih-Jen; Hsyu, Ming-Chun

doi:10.1109/tbme.2007.894731

Cited by 22 publications

(20 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, the sampled version of the surface EMG signals can be treated as discretetime fractional Brownian motion (DFBM). [4][5][6]25 It is clear from Eqs. (2) and (3) that the DFGN can be invoked to find the Hurst parameter H of the DFBM.…”

Section: Fractional Brownian Motion 27mentioning

confidence: 99%

“…6 In order to apply it to the surface EMGs, we have to calculate the DFGNs. This is due to the fact that sampled surface EMGs are modeled as DFBM, but mathematically speaking the DFBM is non-stationary and there is no definition for its spectrum.…”

Section: The Meanings Of Fd In Emgsmentioning

confidence: 99%

“…22 However, recent studies have also illustrated that co-activations of muscles can be present for both normal and spinal cord injured Wistar rats during micturition. [3][4][5][6] The essential difference is that the former case of co-activation is synergic and the latter case dyssynergic. Here, the synergic co-activation is meant to be a state that involved muscles are not only active at the same time, but they also have the (1) same frequencies and (2) persistent signal intensities.…”

Section: Introductionmentioning

confidence: 99%

“…The dyssynergic co-activation is meant to be a state that involved muscles are active with bursting activities, yet either (1) or (2) or both indicators are lacking. 5,6 The importance of having the same frequencies with persistent intensities is equivalent to being synchronized with strength in physiological functions responding to external or internal stimuli. [3][4][5]17 The studies of muscles at forearm and their coactivation in human subjects have been concentrated on the patterns, abnormalities, and adaptions.…”

Section: Introductionmentioning

confidence: 99%

“…First, in order to detect the EMG rhythms, the time-frequency analysis using spectral distribution function (SDF) will be adopted in this paper. 6 Second, since these rhythms are usually embedded in correlated processes and it is well known that the muscular activities can exhibit power-law correlation, [3][4][5][6]19,36 fractal dimensions (FDs) will be invoked to extract the rhythm-dependent signal intensities of EMGs. [3][4][5][6][12][13][14]16,18,20,27,28,38 By combining both the FD and time-frequency analysis, we will investigate if the involved muscles will be under synergic co-activation during voluntary forearm pronation for normal subjects.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Synergic Co-activation in Forearm Pronation

et al. 2008

Self Cite

View full text Add to dashboard Cite

Co-activations of agonist and antagonist muscles are believed to be present in voluntary limb movement. Recent studies indicate that such co-activations are either synergic or dyssynergic. The aims of this paper are to (1) develop a novel method that can extract both the intensity and frequency information from the recordings of the surface electromyograms (EMGs) of involved muscles, and (2) investigate if the involved muscles will be under synergic co-activation during voluntary forearm pronation for normal subjects and dyssynergic co-activation for patients with radial nerve palsy. We examined 11 healthy subjects and 4 patients with right-arm radial nerve palsy in this study. For the group of healthy subjects, each one of them was asked to perform 30 trials of voluntary forearm pronation and then 30 trials of passive pronation as control experiments. As to the second group of patients, each one was asked to perform only 15 trials of voluntary pronation due to the limitation and durability of their arms. The recordings of the surface EMGs included the short and long heads of the biceps brachii, the brachialis, the lateral head of the triceps brachii, brachioradialis, and pronator teres. Experimental results of the healthy group indicated that the surface EMGs of all muscles had no statistically significant changes in fractal dimensions (FDs) and spectral frequencies of the control experiments during passive pronation. Yet, during the voluntary pronation experiments, the surface EMGs of all muscle groups were temporally synchronized in frequencies with persistent intensities. Hence, all involved muscle groups were in synergic co-activation. Statistical results of the group mean values of FDs during rest vs. forearm pronation also revealed significant difference with p < 0.01 for healthy subjects. As to the group of patients, their EMGs could still have bursting activities, but the synchronized significant frequencies might be lacking or the intensities as indicated from their FDs would not be persistent. To further compare the FDs among the three different protocols, a mixed-model ANOVA and multiple comparison tests were performed. Finally, in order to illustrate the advantages of this novel method, we have compared it with the detrended fluctuation analysis (DFA). It is believed that this proposed method will have the potential to be a biomarker for evaluating dynamical disease in neuromuscular disorders.

show abstract