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Corrigendum to "Identification procedure in a model of single fibre action potential - Part I: Estimation of fibre diameter and radial distance" [J. Electromyogr. Kinesiol. 20(2) (2010) 264-273].

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology·2016
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Corrigendum to "Analysis of the peak-to-peak ratio of extracellular potentials in the proximity of excitable fibres" [J. Electromyogr. Kinesiol. 20(5) (2010) 868-878].

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology·2016
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Corrigendum to "Effects of changes in the shape of the intracellular action potential on the peak-to-peak ratio of single muscle fibre potentials" [J. Electromyogr. Kinesiol. 22(1) (2012) 88-97].

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology·2016
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Corrigendum to "Identification procedure in a model of single fibre action potential - Part II: Global approach and experimental results" [J. Electromyogr. Kinesiol. 20(2) (2010) 274-283].

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Corrigendum to "The peak-to-peak ratio of single-fibre potentials is little influenced by changes in the electrode positions close to the muscle fibre" [J. Electromyogr. Kinesiol. 21(3) (2011) 423-432].

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Corrigendum to "Analysis of the relationship between the rise-time and the amplitude of single-fibre potentials in human muscles" [J. Electromyogr. Kinesiol. 20(6) (2010) 1249-1258].

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Related Experiment Video

Updated: Jun 16, 2026

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

[Electromyographic signal processing and analysis methods].

L Gila1, A Malanda, I Rodríguez Carreño

  • 1Serviciio de Neurofisiología Clínica, Hospital Virgen del Camino, Pamplona, Spain. lgilause@cfnavarra.es

Anales Del Sistema Sanitario De Navarra
|January 23, 2010
PubMed
Summary
This summary is machine-generated.

Clinical electromyography (EMG) records muscle activity for diagnosing neuromuscular disorders. Advances in technology have led to more accurate digital systems, with future applications including AI for signal analysis and diagnosis support.

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Last Updated: Jun 16, 2026

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Clinical Diagnostics

Context:

  • Clinical electromyography (EMG) is a diagnostic tool for skeletal muscle bioelectrical activity.
  • Technological advancements have significantly improved EMG capabilities since its inception.
  • Early EMG devices utilized analog circuits, while modern systems employ digital technology.

Purpose:

  • To outline the evolution of clinical electromyography.
  • To highlight the impact of technological progress on EMG diagnostic performance.
  • To project future advancements in EMG, including AI integration.

Summary:

  • Electromyography (EMG) analyzes skeletal muscle bioelectrical signals for diagnosing neuromuscular conditions.
  • The development from analog to digital technology has enhanced EMG system accuracy and functionality.
  • Future directions include artificial intelligence for automated signal classification and expert systems for diagnostic support.

Impact:

  • Improved accuracy and efficiency in diagnosing neuromuscular pathologies.
  • Potential for AI-driven diagnostic support systems to enhance clinical decision-making.
  • Broadened applications of EMG in medical diagnostics through technological integration.