ArticleHeliyon2024
Identification of electromyographic patterns of bradykinesia in patients with Parkinson's disease.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Surface Electromyography for Parkinson's Disease Monitoring: A Review of Machine and Deep Learning Techniques.Sensors (Basel, Switzerland) · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Parkinson's disease (PD) is a common neurodegenerative disease characterized by rest tremor, rigidity, and bradykinesia. Assessing the severity of these symptoms is typically done using the third part of the Movement Disorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS III), relying on subjective evaluations by neurologists, which may lead to challenges in result interpretation. To address this issue, incorporation of surface electromyography (sEMG) in diagnostics. Objectives: The aim of the study is to search for specific sEMG patterns that allow assessing the severity of bradykinesia. Method: This case-control study involved 8 patients with PD at Hoehn & Yahr stages 2-3, and 7 healthy volunteers. sEMG was measured while the subjects performed the "finger tapping" and "hand movements" tests of the MDS-UPDRS III. The tests were conducted both before and after levodopa intake to identify patterns indicating changes in motor functions. During the tests, we observed the peak activity of the sEMG signal, reflecting the moments of activation of individual muscle groups involved in the implementation of the movement. Peak activity was characterized by the total number of maximum sEMG signal extrema and the distance between them. The data were compared with the healthy group. Results: Peak activity increased after levodopa intake, indicating a reduction in bradykinesia. This feature partially correlates with clinicians' assessments and enhances the similarity of predictions by the MDS-UPDRS III scoring model to physician scores. Conclusions: The results show the effectiveness of using sEMG and the number of peaks corresponding to the moments of muscle activation to assess bradykinesia.
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Registered trials
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