PS-33-2 Quantitative EMG in the diagnosis of neuromuscular disorders

Wimalaratna, Sunil; Tooley, Mark; Churchill, E.; Preece, AW; Morgan, M.

doi:10.1016/0924-980x(95)93045-u

Cited by 10 publications

(9 citation statements)

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“…The bipolar recording montage was unable to differentiate neurogenic from myopathic disorders in the disease group, whereas concomitant nEMG distinguished between control subjects and patients with neuromuscular disease as well as between neurogenic and myopathic conditions. 22 Multielectrode array sEMG was able to distinguish between myopathic and neuropathic disorders (Duchenne and Becker muscular dystrophy, spinal muscular atrophy, and hereditary motor and sensory neuropathy) and healthy control subjects with sensitivities and specificities of detecting neuromuscular disease (82% and 97%, respectively), primary myopathic disorders (85% and 97%, respectively), and primary neuropathic disorders (68% and 98%, respectively), comparable to historical results for conventional nEMG. 11 Four class III studies evaluated the utility of sEMG for detection of myoelectric signal abnormalities in the more specific neuromuscular disorders of acquired demyelinating peripheral neuropathy (ADP), amyotrophic lateral sclerosis (ALS), hypokalemic periodic paralysis (HOPP), and post-poliomyelitis syndrome (PPS).…”

Section: Clinical Utility Of Semg In Diagnosis Of Neuromuscularsupporting

confidence: 65%

American Association of Neuromuscular & Electrodiagnostic Medicine evidenced‐based review: Use of surface electromyography in the diagnosis and study of neuromuscular disorders

Meekins

Quan

2008

Muscle and Nerve

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Surface electromyography (sEMG) measures myoelectrical signals recorded from sensors placed on the skin surface. The non-invasive nature of sEMG makes it a potentially useful technology for studying diseases of muscle and nerve. Reviews published by the American Association of Neuromuscular and Electrodiagnostic Medicine (AANEM) and the Therapeutics and Technology Assessment Subcommittee of the American Academy of Neurology (AAN), covering 1964-1994 and 1952-1998, respectively, concluded that sEMG adds no clinical utility over conventional needle EMG (nEMG) for the diagnosis of neuromuscular disease. The AANEM sEMG task force reevaluated the diagnostic utility and added value of this technology for the study of neuromuscular disease based on a contemporary review of relevant literature published between January 1994 and February 2006. The present review concludes that sEMG may be useful to detect the presence of neuromuscular disease (level C rating, class III data), but there are insufficient data to support its utility for distinguishing between neuropathic and myopathic conditions or for the diagnosis of specific neuromuscular diseases. sEMG may be useful for additional study of fatigue associated with post-poliomyelitis syndrome and electromechanical function in myotonic dystrophy (level C rating, class III data).

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Section: Clinical Utility Of Semg In Diagnosis Of Neuromuscularsupporting

confidence: 65%

American Association of Neuromuscular & Electrodiagnostic Medicine evidenced‐based review: Use of surface electromyography in the diagnosis and study of neuromuscular disorders

Meekins

Quan

2008

Muscle and Nerve

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“…A high-density surface EMG (HD-sEMG) 2 is a form of sEMG where the measurement is typically acquired via a two-dimensional grid of electrodes placed on the skin of the subject. Because sEMG is a non-invasive technique it has since long been successfully applied in clinical routine, most notably for diagnosis of neuromuscular disease 3 . Since EMG is a predictor of muscle forces 4 , an alternative use of sEMG is as a control signal for a system which transforms the myoelectric signal into an executable command for a device, such as a prosthesis 5 , an exoskeleton 6 or a video game 7 .…”

Section: Introductionmentioning

confidence: 99%

Extraction of Multi-Labelled Movement Information from the Raw HD-sEMG Image with Time-Domain Depth

Olsson

Sager

Andersson

et al. 2019

Sci Rep

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In contemporary muscle-computer interfaces for upper limb prosthetics there is often a trade-off between control robustness and range of executable movements. As a very low movement error rate is necessary in practical applications, this often results in a quite severe limitation of controllability; a problem growing ever more salient as the mechanical sophistication of multifunctional myoelectric prostheses continues to improve. A possible remedy for this could come from the use of multi-label machine learning methods, where complex movements can be expressed as the superposition of several simpler movements. Here, we investigate this claim by applying a multi-labeled classification scheme in the form of a deep convolutional neural network (CNN) to high density surface electromyography (HD-sEMG) recordings. We use 16 independent labels to model the movements of the hand and forearm state, representing its major degrees of freedom. By training the neural network on 16 × 8 sEMG image sequences 24 samples long with a sampling rate of 2048 Hz to detect these labels, we achieved a mean exact match rate of 78.7% and a mean Hamming loss of 2.9% across 14 healthy test subjects. With this, we demonstrate the feasibility of highly versatile and responsive sEMG control interfaces without loss of accuracy.

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“…Diferentes tipos de análisis tiempo-frecuencia, como las transformadas wavelet (TW) o la distribución de Choi-Williams, se han aplicado a la señal EMGs durante la contracción muscular para estudiar la fatiga y las relaciones entre la actividad eléctrica y mecánica del músculo 15,16 . Por otra parte, están apareciendo los primeros trabajos de EMGs aplicada al diagnóstico de enfermedades neuromusculares: mediante algoritmos basados en clustering, análisis de componentes principales de coeficientes wavelet (wavelet spectrum matching) 17 y otras técnicas, se está intentando descomponer la señal de superficie para extraer PAUMs aislados y otros datos de valor diagnóstico en patologías como radiculopatías lumbosacras 18,19 .…”

Section: Técnicas Especiales De Procesado De Señales Electromiográficasunclassified

Electromyographic signal processing and analysis methods

Gila

Malanda

Carreño³

et al. 2010

An Sist Sanit Navar

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RESUMENLa electromiografía clínica es una metodología de registro y análisis de la actividad bioeléctrica del músculo esquelético orientada al diagnóstico de las enfermedades neuromusculares. Las posibilidades de aplicación y el rendimiento diagnóstico de la electromiografía han evolucionado paralelamente al conocimiento de las propiedades de la energía eléctrica y al desarrollo de la tecnología eléctrica y electrónica. A mediados del siglo XX se introdujo el primer equipo comercial de electromiografía para uso médico basado en circuitos electrónicos analógicos. El desarrollo posterior de la tecnología digital ha permitido disponer de sistemas controlados por microprocesadores cada vez más fiables y potentes para captar, representar, almacenar, analizar y clasificar las señales mioeléctricas. Es esperable que el avance de las nuevas tecnologías de la información y la comunicación pueda conducir en un futuro próximo a la aplicación de desarrollos de inteligencia artificial que faciliten la clasificación automá-tica de señales así como sistemas expertos de apoyo al diagnóstico electromiográfico.Palabras clave. Electromiografía. Potencial de acción de unidad motora. Procesamiento de señal. Estudios neurofisiológicos. ABSTRACTClinical electromyography is a methodology for recording and analysing the bioelectrical activity of the skeletal muscle tissue in order to diagnose neuromuscular pathology. The possibilities of application and the diagnostic performance of electromyography have evolved parallel to a growing understanding of the properties of electricity and the development of electrical and electronic technology. The first commercially available electromyography equipment for medical use was introduced in the middle of the 20th century. It was based on analog electronic circuits. The subsequent development of digital technology made available more powerful and accurate systems, controlled by microprocessors, for recording, displaying, storing, analysing, and classifying the myoelectric signals. In the near future, it is likely that advances in the new information and communication technologies could result in the application of artificial intelligence systems to the automatic classification of signals as well as expert systems for electromyographic diagnosis support.

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PS-33-2 Quantitative EMG in the diagnosis of neuromuscular disorders

Cited by 10 publications

References 0 publications

American Association of Neuromuscular & Electrodiagnostic Medicine evidenced‐based review: Use of surface electromyography in the diagnosis and study of neuromuscular disorders

American Association of Neuromuscular & Electrodiagnostic Medicine evidenced‐based review: Use of surface electromyography in the diagnosis and study of neuromuscular disorders

Extraction of Multi-Labelled Movement Information from the Raw HD-sEMG Image with Time-Domain Depth

Electromyographic signal processing and analysis methods

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