Three dimensional innervation zone imaging in spastic muscles of stroke survivors

Zhang, Chuan; Chen, Yen‐Ting; Liu, Yang; Zhou, Ping; Li, Sheng; Zhang, Yingchun

doi:10.1088/1741-2552/ab0fe1

Cited by 14 publications

(14 citation statements)

References 42 publications

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“…A textbook is available with the location of the IZs of 43 muscles ( 19 ). Knowledge of the location of the IZs of a muscle is clinically important for (a) proper positioning of a single electrode pair between the IZ and tendon junctions, (b) targeted injection of botulinum toxin ( 58 , 122 ), and (c) programming surgery in a way that would avoid damage to muscle innervation. The latter application is particularly important for reducing the risk of anal sphincter partial denervation resulting from episiotomy ( 18 , 51 , 123 – 125 ).…”

Section: Surface Emg Applicationsmentioning

confidence: 99%

Surface EMG in Clinical Assessment and Neurorehabilitation: Barriers Limiting Its Use

et al. 2020

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This article addresses the potential clinical value of techniques based on surface electromyography (sEMG) in rehabilitation medicine with specific focus on neurorehabilitation. Applications in exercise and sport pathophysiology, in movement analysis, in ergonomics and occupational medicine, and in a number of related fields are also considered. The contrast between the extensive scientific literature in these fields and the limited clinical applications is discussed. The “barriers” between research findings and their application are very broad, and are longstanding, cultural, educational, and technical. Cultural barriers relate to the general acceptance and use of the concept of objective measurement in a clinical setting and its role in promoting Evidence Based Medicine. Wide differences between countries exist in appropriate training in the use of such quantitative measurements in general, and in electrical measurements in particular. These differences are manifest in training programs, in degrees granted, and in academic/research career opportunities. Educational barriers are related to the background in mathematics and physics for rehabilitation clinicians, leading to insufficient basic concepts of signal interpretation, as well as to the lack of a common language with rehabilitation engineers. Technical barriers are being overcome progressively, but progress is still impacted by the lack of user-friendly equipment, insufficient market demand, gadget-like devices, relatively high equipment price and a pervasive lack of interest by manufacturers. Despite the recommendations provided by the 20-year old EU project on “Surface EMG for Non-Invasive Assessment of Muscles (SENIAM),” real international standards are still missing and there is minimal international pressure for developing and applying such standards. The need for change in training and teaching is increasingly felt in the academic world, but is much less perceived in the health delivery system and clinical environments. The rapid technological progress in the fields of sensor and measurement technology (including sEMG), assistive devices, and robotic rehabilitation, has not been driven by clinical demands. Our assertion is that the most important and urgent interventions concern enhanced education, more effective technology transfer, and increased academic opportunities for physiotherapists, occupational therapists, and kinesiologists.

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Section: Surface Emg Applicationsmentioning

confidence: 99%

Surface EMG in Clinical Assessment and Neurorehabilitation: Barriers Limiting Its Use

et al. 2020

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“…While the TMSi system has been widely used in previous studies, 17 – 20 no studies have analyzed the influence of the common component that is caused by the TMSi system on the classification of movements. Thus, three datasets, including the original dataset, the dataset with the global common component, and the dataset with the selected common component were constructed to assess the influence of the common component.…”

Section: Discussionmentioning

confidence: 99%

The influence of common component on myoelectric pattern recognition

et al. 2020

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Using the Twente Medical Systems international B.V. (TMSi) electromyography (EMG) system, active signal shielding was applied to clean signals and create data without interference and cable movement artifacts. TMSi, used in high-density surface EMG pattern recognition, controls myoelectric rehabilitation robots, yet few have studied how active signal shielding influences pattern recognition. This study aimed to investigate how active signal shielding used within the TMSi influenced motion pattern recognition. Methods: Surface EMG of dominant side forearm and hand muscles was studied in eight healthy participants. The common component's influence was accessed by the classification performance of wrist and hand functional movements. Results: The classification performance of EMG signals with the common component was obviously lower than signals without the common component using one to five electrodes. Conversely, a higher motion classification performance was achieved using signals with the common component using over 12 electrodes. Optimal channel distribution was examined based on the sequential feed-forward selection method, showing that the common component affected the optimal channel location. Conclusions: Active signal shielding in the TMSi improved classification accuracy in motion pattern recognition when over 12 electrodes were used. The optimal channel distribution was related to the common component when using the TMSi.

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“…In addition, the ability to detect the IZ has important clinical value. For example, one clinical application of IZ estimation is to guide botulinum toxin (BTX) injection more precisely for treating spasticity in patients with neurological injuries such as stroke and cerebral palsy ( Van Campenhout and Molenaers, 2011 ; Guzmán-Venegas et al, 2014 ; Zhang et al, 2019 ; Chen et al, 2020 ; Zhang et al, 2021 ). The effectiveness of BTX treatment has been reported to depend on the distance between the injection site and the IZ ( Shaari and Ira Sanders, 1993 ; Lapatki et al, 2011 ; Kaymak et al, 2018 ).…”

Section: Introductionmentioning

confidence: 99%

Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform

Huang

Lu²,

Chen³

et al. 2023

Front. Physiol.

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This study examined methods for estimating the innervation zone (IZ) of a muscle using recorded monopolar high density M waves. Two IZ estimation methods based on principal component analysis (PCA) and Radon transform (RT) were examined. Experimental M waves, acquired from the biceps brachii muscles of nine healthy subjects were used as testing data sets. The performance of the two methods was evaluated by comparing their IZ estimations with manual IZ detection by experienced human operators. Compared with manual detection, the agreement rate of the estimated IZs was 83% and 63% for PCA and RT based methods, respectively, both using monopolar high density M waves. In contrast, the agreement rate was 56% for cross correlation analysis using bipolar high density M waves. The mean difference in estimated IZ location between manual detection and the tested method was 0.12 ± 0.28 inter-electrode-distance (IED) for PCA, 0.33 ± 0.41 IED for RT and 0.39 ± 0.74 IED for cross correlation-based methods. The results indicate that the PCA based method was able to automatically detect muscle IZs from monopolar M waves. Thus, PCA provides an alternative approach to estimate IZ location of voluntary or electrically-evoked muscle contractions, and may have particular value for IZ detection in patients with impaired voluntary muscle activation.

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Three dimensional innervation zone imaging in spastic muscles of stroke survivors

Cited by 14 publications

References 42 publications

Surface EMG in Clinical Assessment and Neurorehabilitation: Barriers Limiting Its Use

Surface EMG in Clinical Assessment and Neurorehabilitation: Barriers Limiting Its Use

The influence of common component on myoelectric pattern recognition

Muscle innervation zone estimation from monopolar high-density M-waves using principal component analysis and radon transform

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