Assessing disease and wellness in the occupational setting: Electrodiagnostic functional assessment from wired to wireless

Seidner, Adam

doi:10.1109/iembs.2011.6091867

Cited by 1 publication

(3 citation statements)

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“…The data analysis algorithms presented in this manuscript were developed by relying on a database of surface EMG recordings previously collected during functional evaluations performed using an approach referred to as the Electrodiagnostic Functional Assessment (EFA) [34–36]. This section provides a description of the protocols utilized to collect the EMG data and describes the algorithms developed in the study.…”

Section: Methodsmentioning

confidence: 99%

“…An EMG expert had previously annotated the surface EMG recordings performed during the above-described EFA-based protocols [34–36] using criteria commonly used in a clinical context to capture the level of muscle activity (including its laterality), the presence/absence and severity of muscle spasms, and the muscles’ motor unit recruitment characteristics associated with the EMG amplitude modulation. Clinical scores were generated via visual inspection of the EMG data using an ordinal scale from 0 to 10 designed to capture the magnitude of each phenomenon of interest (e.g., an activity level score equal to 0 was associated with recordings marked by very low amplitude of the EMG data, whereas an activity level score equal to 10 was associated with recordings marked by very large ampitude of the EMG data).…”

Section: Methodsmentioning

confidence: 99%

“…This is relevant because the analysis of EMG recordings via their visual inspection is time-consuming, a factor that significantly hinders the use of EMG recordings in the clinic. To address this problem, algorithms were developed using an existing database of surface EMG recordings collected during functional evaluations performed using an approach referred to as the Electrodiagnostic Functional Assessment (EFA) [34–36]. The complexity of the algorithms varied according to the complexity of the clinical observations performed via visual inspection of the EMG recordings.…”

Section: Introductionmentioning

confidence: 99%

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Assessing aberrant muscle activity patterns via the analysis of surface EMG data collected during a functional evaluation

Golabchi

Sapienza

Severini

et al. 2019

BMC Musculoskelet Disord

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BackgroundSurface electromyographic (EMG) recordings collected during the performance of functional evaluations allow clinicians to assess aberrant patterns of muscle activity associated with musculoskeletal disorders. This assessment is typically achieved via visual inspection of the surface EMG data. This approach is time-consuming and leads to accurate results only when the assessment is carried out by an EMG expert.MethodsA set of algorithms was developed to automatically evaluate aberrant patterns of muscle activity. EMG recordings collected during the performance of functional evaluations in 62 subjects (22 to 61 years old) were used to develop and characterize the algorithms. Clinical scores were generated via visual inspection by an EMG expert using an ordinal scale capturing the severity of aberrant patterns of muscle activity. The algorithms were used in a case study (i.e. the evaluation of a subject with persistent back pain following instrumented lumbar fusion who underwent lumbar hardware removal) to assess the clinical suitability of the proposed technique.ResultsThe EMG-based algorithms produced accurate estimates of the clinical scores. Results were primarily obtained using a linear regression approach. However, when the results were not satisfactory, a regression implementation of a Random Forest was utilized, and the results compared with those obtained using a linear regression approach. The root-mean-square error of the clinical score estimates produced by the algorithms was a small fraction of the ordinal scale used to rate the severity of the aberrant patterns of muscle activity. Regression coefficients and associated 95% confidence intervals showed that the EMG-based estimates fit well the clinical scores generated by the EMG expert. When applied to the clinical case study, the algorithms appeared to capture the characteristics of the muscle activity patterns associated with persistent back pain following instrumented lumbar fusion.ConclusionsThe proposed approach relies on EMG-based measures to generate accurate estimates of the severity of aberrant patterns of muscle activity. The results obtained in the case study suggest that the proposed technique is suitable to derive clinically-relevant information from EMG data collected during functional evaluations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%