Differences in EMG spike shape between individuals with and without non-specific arm pain

Calder, Kristina M.; Gabriel, David A.; McLean, Linda

doi:10.1016/j.jneumeth.2008.11.015

Cited by 8 publications

(5 citation statements)

References 33 publications

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“…Further information about muscle activation and motor unit activity can be assessed from the morphology of the sEMG signal with spike shape analysis (SSA) features [21]. A spike is defined as a single upward and downward deflection that is greater than a predefined threshold amplitude.…”

Section: Introductionmentioning

confidence: 99%

“…Several sEMG features have been found to exhibit differences between healthy subjects and patients with neuromuscular or muscle disorders including Duchenne muscular dystrophy [24], non-specific arm pain [21], stiff elbow [9], and elbow trauma [25].…”

Section: Introductionmentioning

confidence: 99%

“…Spike shape analysis was implemented for identifying and evaluating patients of non-specific arm pain. Significant increases in mean spike amplitude (MSA), mean spike frequency (MSF), mean spike slope (MSS), and mean number of peaks per spike (MNPPS) and significant decreases in mean spike duration (MSD) were observed in the sEMG signals collected from the extensor carpi radialis muscle in subjects with non-specific arm pain compared to controls [21].…”

Section: Introductionmentioning

confidence: 99%

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Development of an EMG-Based Muscle Health Model for Elbow Trauma Patients

Farago

Chinchalkar

Lizotte

et al. 2019

Sensors

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Wearable robotic braces have the potential to improve rehabilitative therapies for patients suffering from musculoskeletal (MSK) conditions. Ideally, a quantitative assessment of health would be incorporated into rehabilitative devices to monitor patient recovery. The purpose of this work is to develop a model to distinguish between the healthy and injured arms of elbow trauma patients based on electromyography (EMG) data. Surface EMG recordings were collected from the healthy and injured limbs of 30 elbow trauma patients while performing 10 upper-limb motions. Forty-two features and five feature sets were extracted from the data. Feature selection was performed to improve the class separation and to reduce the computational complexity of the feature sets. The following classifiers were tested: linear discriminant analysis (LDA), support vector machine (SVM), and random forest (RF). The classifiers were used to distinguish between two levels of health: healthy and injured (50% baseline accuracy rate). Maximum fractal length (MFL), myopulse percentage rate (MYOP), power spectrum ratio (PSR) and spike shape analysis features were identified as the best features for classifying elbow muscle health. A majority vote of the LDA classification models provided a cross-validation accuracy of 82.1%. The work described in this paper indicates that it is possible to discern between healthy and injured limbs of patients with MSK elbow injuries. Further assessment and optimization could improve the consistency and accuracy of the classification models. This work is the first of its kind to identify EMG metrics for muscle health assessment by wearable rehabilitative devices.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development of an EMG-Based Muscle Health Model for Elbow Trauma Patients

Farago

Chinchalkar

Lizotte

et al. 2019

Sensors

View full text Add to dashboard Cite

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“…This hypothesis is supported by findings of altered vibration thresholds [22,23] and neural tissue sensitivity [22,24]. However, data also exist suggesting that a muscle tissue disorder [25,26] might be a prevalent pathology in NSAP. Recently, we presented data that widespread sensory hypersensitivity along with localised neural tissue sensitivity were characteristic features in this condition [27].…”

Section: Introductionmentioning

confidence: 81%

The clinical utility of pain classification in non-specific arm pain

et al. 2015

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Mechanisms-based pain classification has received considerable attention recently for its potential use in clinical decision making. A number of algorithms for pain classification have been proposed. Non-specific arm pain (NSAP) is a poorly defined condition, which could benefit from classification according to pain mechanisms to improve treatment selection. This study used three published classification algorithms (hereafter called NeuPSIG, Smart, Schafer) to investigate the frequency of different pain classifications in NSAP and the clinical utility of these systems in assessing NSAP. Forty people with NSAP underwent a clinical examination and quantitative sensory testing. Findings were used to classify participants according to three classification algorithms. Frequency of pain classification including number unclassified was analysed using descriptive statistics. Inter-rater agreement was analysed using kappa coefficients. NSAP was primarily classified as 'unlikely neuropathic pain' using NeuPSIG criteria, 'peripheral neuropathic pain' using the Smart classification and 'peripheral nerve sensitisation' using the Schafer algorithm. Two of the three algorithms allowed classification of all but one participant; up to 45% of participants (n = 18) were categorised as mixed by the Smart classification. Inter-rater agreement was good for the Schafer algorithm (к = 0.78) and moderate for the Smart classification (к = 0.40). A kappa value was unattainable for the NeuPSIG algorithm but agreement was high. Pain classification was achievable with high inter-rater agreement for two of the three algorithms assessed. The Smart classification may be useful but requires further direction regarding the use of clinical criteria included. The impact of adding a pain classification to clinical assessment on patient outcomes needs to be evaluated.

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“…EMG findings in NSAP may point to pain that is myopathic 77,78 and as such nociceptive. However, a neurogenic origin has also been suggested.…”

Section: Emg Studies and Morphological Changesmentioning

confidence: 99%

Pathophysiology of non-specific arm pain

Moloney

Hall²,

Doody

2011

Physical Therapy Reviews

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Background: Non-specific arm pain (NSAP) is a vague clinical entity which may constitute up to 53% of work related upper limb disorders. The pathophysiology of NSAP is poorly understood with hypotheses ranging from disturbance of muscle function and neuropathic arm pain to central sensitization.Objectives: The purpose of this review was to investigate the current level of evidence to support three main classifications of pain (peripheral neuropathic pain, nociceptive pain and central sensitization) in NSAP. Major findings: There is evidence in the literature to support each of the three classifications of pain; however, this evidence is weak. No one classification of pain is better supported than another. It is also likely that overlap exists between pain classifications which present in NSAP. Conclusion: Different pain classifications are likely to exist in NSAP and indeed may co-exist. In the absence of definitive support for one classification over another, clinicians should be encouraged to perform thorough pain assessments in individuals with NSAP in order to optimize clinical decision making.

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Differences in EMG spike shape between individuals with and without non-specific arm pain

Cited by 8 publications

References 33 publications

Development of an EMG-Based Muscle Health Model for Elbow Trauma Patients

Development of an EMG-Based Muscle Health Model for Elbow Trauma Patients

The clinical utility of pain classification in non-specific arm pain

Pathophysiology of non-specific arm pain

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