Effect of medication in Parkinson’s disease: a wavelet analysis of EMG signals

Strambi, Soo-Kyung; Rossi, Bruno; Michele, G. De; Sello, Stefano

doi:10.1016/j.medengphy.2004.01.006

Cited by 19 publications

(14 citation statements)

References 13 publications

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“…Thus, the sEMG index may be a potent complement to the other measures as it may provide insight into gait abnormalities at the level of muscle impairment, such as differences in muscle activation and decreased central drive [34,35], muscle spasticity [36], and changes in muscle fiber composition [9] which have been reported in CP. This has already been reported with wavelet analysis of sEMG in Parkinson's disease to assess the effects of medication [37]. The index and its sub-assessment steps could be employed to help determine which children will best benefit from interventions where the importance of sEMG patterns has already been reported such as for rectus femoris transfer surgery [38] or dorsal rhizotomy [39].…”

Section: Discussionmentioning

confidence: 95%

A time–frequency based electromyographic analysis technique for use in cerebral palsy

Lauer¹,

Stackhouse²,

Shewokis³

et al. 2007

Gait & Posture

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Section: Discussionmentioning

confidence: 95%

A time–frequency based electromyographic analysis technique for use in cerebral palsy

Lauer¹,

Stackhouse²,

Shewokis³

et al. 2007

Gait & Posture

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“…The following function provides a suitable compromise in both time and frequency to extract features from both EEG and EMG signals (Samar et al, 1999;Strambi et al, 2004):…”

Section: Methodsmentioning

confidence: 99%

Existing Motor State Is Favored at the Expense of New Movement during 13-35 Hz Oscillatory Synchrony in the Human Corticospinal System

Gilbertson¹,

Lalo²,

Doyle³

et al. 2005

J. Neurosci.

336

265

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Oscillations in local field potentials in the ␤-frequency band (13-35 Hz) are a pervasive feature of human and nonhuman primate motor cortical areas. However, the function of such synchronous activity across populations of neurons remains unknown. Here, we test the hypothesis that ␤ activity may promote existing motor set and posture while compromising processing related to new movements. Three experiments were performed. First, healthy subjects were instructed to make reaction time movements of the outstretched index finger in response to imperative cues triggered by transient increases in corticospinal synchrony, as evidenced by phasic elevations of ␤-frequency band microtremor and intermuscular synchrony. Second, healthy subjects were instructed to resist a stretch to the index finger triggered in the same way. Finger acceleration in the reaction time task and transcortical components of the stretch reflex were measured and compared with those elicited by random cue or stretch presentation. Finally, we sought a correlation between finger acceleration in the reaction time task and cortical synchrony directly measured from the electrocorticogram in two patients undergoing functional neurosurgery. We demonstrate that movements are slowed and transcortical responses to stretch are potentiated during periods of elevated ␤-band cortical synchrony. The results suggest that physiological periods of ␤ synchrony are associated with a cortical state in which postural set is reinforced, but the speed of new movements impaired. The findings are of relevance to Parkinson's disease, in which subcortical and cortical ␤-band synchronization is exaggerated in the setting of increased tone and slowed movements.

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“…It has been observed that the medication and DBS may modify the tremor amplitude, regularity and frequency [4,41,42], movement speed [3,8,34,40,44,49,51,52], joint kinetics and muscle activation during movements [55], EMG burst patterns during movement [34,51,52] and the cortico-muscular coherence [25,37]. There is currently a lot of interest for characterizing EMG and kinematic signals of PD patients.…”

Section: Surface Electromyography and Kinematic Measurements In Pdmentioning

confidence: 99%

“…In [29], the discrete Morlet wavelet was used for analysis as in many other EMG studies [10,20,40]. The scalograms (11) were calculated from the EMG signals of both sides of the body and the cross-scalogram (12) between the right and left side signals.…”

Section: Spectral-based Parametersmentioning

confidence: 99%

Feature Extraction Methods for Studying Surface Electromyography and Kinematic Measurements in Parkinson’s Disease

Rissanen¹,

Kankaanpää²,

Karjalainen³

2012

Computational Intelligence in Electromyography Analysis - A Perspective on Current Applications and Future Challenges

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Effect of medication in Parkinson’s disease: a wavelet analysis of EMG signals

Cited by 19 publications

References 13 publications

A time–frequency based electromyographic analysis technique for use in cerebral palsy

A time–frequency based electromyographic analysis technique for use in cerebral palsy

Existing Motor State Is Favored at the Expense of New Movement during 13-35 Hz Oscillatory Synchrony in the Human Corticospinal System

Feature Extraction Methods for Studying Surface Electromyography and Kinematic Measurements in Parkinson’s Disease

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