Literature DB >> 25583618

Comparison of frequency and time domain electromyography parameters in women with patellofemoral pain.

Ronaldo Valdir Briani1, Danilo de Oliveira Silva2, Marcella Ferraz Pazzinatto2, Carlos Eduardo de Albuquerque3, Deisi Ferrari4, Fernando Amâncio Aragão3, Fábio Mícolis de Azevedo5.   

Abstract

BACKGROUND: Despite its high incidence, patellofemoral pain etiology remains unclear. No prior study has compared surface electromyography frequency domain parameters and surface electromyography time domain variables, which have been used as a classic analysis of patellofemoral pain.
METHODS: Thirty one women with patellofemoral pain and twenty eight pain-free women were recruited. Each participant was asked to descend a seven step staircase and data from five successful trials were collected. During the task, the vastus medialis and vastus lateralis muscle activities were monitored by surface electromyography. The data were processed and analyzed in four variables of the frequency domain (median frequency, low, medium and high frequency bands) and three time domain variables (Automatic, Cross-correlation and Visual Onset between the vastus medialis and vastus lateralis muscles). Reliability, Receiver Operating Characteristic curves and regression models were performed.
FINDINGS: The medium frequency band was the most reliable variable and different between the groups for both muscles, also demonstrated the best values of sensitivity and sensibility, 72% and 69% for the vastus medialis and 68% and 62% for the vastus lateralis, respectively. The frequency variables predicted the pain of individuals with patellofemoral pain, 26% for the vastus medialis and 20% for the vastus lateralis, being better than the time variables, which achieved only 7%.
INTERPRETATION: The frequency domain parameters presented greater reliability, diagnostic accuracy and capacity to predict pain than the time domain variables during stair descent and might be a useful tool to diagnose individuals with patellofemoral pain.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Diagnosis; Linear models; ROC curves; Reproducibility of results

Mesh:

Year:  2015        PMID: 25583618     DOI: 10.1016/j.clinbiomech.2014.12.014

Source DB:  PubMed          Journal:  Clin Biomech (Bristol, Avon)        ISSN: 0268-0033            Impact factor:   2.063


  3 in total

1.  Diagnosis of Patellofemoral Pain Syndrome Based on a Multi-Input Convolutional Neural Network With Data Augmentation.

Authors:  Wuxiang Shi; Yurong Li; Baoping Xiong; Min Du
Journal:  Front Public Health       Date:  2021-02-11

2.  Different pain responses to distinct levels of physical activity in women with patellofemoral pain.

Authors:  Ronaldo V Briani; Marcella F Pazzinatto; Danilo De Oliveira Silva; Fábio M Azevedo
Journal:  Braz J Phys Ther       Date:  2017-03-17       Impact factor: 3.377

3.  Auxiliary Diagnostic Method for Patellofemoral Pain Syndrome Based on One-Dimensional Convolutional Neural Network.

Authors:  Wuxiang Shi; Yurong Li; Dujian Xu; Chen Lin; Junlin Lan; Yuanbo Zhou; Qian Zhang; Baoping Xiong; Min Du
Journal:  Front Public Health       Date:  2021-04-16
  3 in total

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