帕金森病患者行走过程中简化的肌肉补充策略:肌电图信号的时频分析

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2023-09-04 DOI:10.1016/j.irbm.2023.100798
Francesco Di Nardo , Marco Romanato , Fabiola Spolaor , Daniele Volpe , Sandro Fioretti , Zimi Sawacha
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引用次数: 0

摘要

尽管步态分析已被广泛用于描述帕金森病(PD)在行走过程中的功能障碍,但很少有人致力于了解肌肉活动的作用。本研究旨在基于对下肢肌肉表面肌电信号的连续小波变换(CWT)分析,在时频域中表征步行过程中下肢肌肉的募集。材料和方法在步态过程中采集20例帕金森病患者和10例年龄匹配的健康对照(HC)的胫骨前肌(TA)、腓肠肌外侧肌(GL)、股直肌(RF)和股二头肌(BF)的肌电信号。应用基于CWT的方法来处理sEMG信号,以评估发生频率(OF,即每次肌肉激活发生的步幅百分比)和每次肌肉激活的频率含量(以Hz为单位)。这些参数很少在PD中量化。结果与HC相比,PD患者在RF、BF和TA上的中值OF显著降低(p<0.05),表明有减少下肢肌肉整体受累的趋势。在同一群体内的肌肉之间没有检测到OF的显著差异(p>0.05)。PD中的频率含量没有显著变化(p>0.05)。结论该分析表明,PD患者的特征是那些通常用于精细控制身体节段运动的肌肉的募集减少,同时主要参与运动的肌肉募集增加。肌纤维募集的实质性改变与帕金森病无关。这些发现表明,与HC相比,帕金森病患者在行走过程中倾向于采用更简单的肌肉募集策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Simplified Muscle-Recruitment Strategy During Walking in Parkinson's Disease People: A Time-Frequency Analysis of EMG Signal

Simplified Muscle-Recruitment Strategy During Walking in Parkinson's Disease People: A Time-Frequency Analysis of EMG Signal

Objective

Although gait analysis has been widely adopted to describe Parkinson's disease (PD) dysfunctions during walking, few efforts have been made to understand muscle activity role. The current study aims to characterize lower-limb-muscle recruitment during walking in time-frequency domain, based on Continuous Wavelet Transform (CWT) analysis of surface-electromyography (sEMG) signal from lower-limb muscles.

Materials and methods

sEMG signals from Tibialis Anterior (TA), Gastrocnemius Lateralis (GL), Rectus Femoris (RF), and Biceps Femoris (BF) of 20 people with PD and 10 age-matched healthy controls (HC) were acquired during gait. sEMG signals were processed applying a CWT-based approach to assess the occurrence frequency (OF, i.e., the percentage of strides of each muscle activation occurrence) and the frequency content of each muscle activation (in Hz). These parameters are rarely quantified in PD.

Results

Compared to HC, people with PD displayed a significant decrease (p<0.05) in median OF on RF, BF, and TA, indicating a tendency to reduce the global involvement of lower-limb muscles. No significant differences (p>0.05) in OF were detected among muscle within the same population. No significant changes (p>0.05) in frequency content were revealed in PD.

Conclusion

This analysis suggests that people with PD are characterized by a reduced recruitment of those muscles typically adopted to finely control body-segment motion and a concomitant increased recruitment of those muscles mainly involved in locomotion. No substantial alteration in recruiting muscle fibers is associated with PD. These findings suggest that people with PD are inclined to adopt simpler muscular-recruitment strategies during walking, compared to HC.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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