Literature DB >> 31335345

Quantitative Evaluation of Interference Patterns on Electromyography in Neuropathy.

Yukiko Kobayashi1, Kazuto Akaboshi, Osamu Takahashi, Meigen Liu.   

Abstract

OBJECTIVE: The aim of this study was to develop a quantitative evaluation method of interference patterns on needle electromyography that is easy to apply in clinical use and to examine its usefulness. Diagnostic electrophysiological assessments are important for physiatrists, and correct diagnosis and assessment are essential for proper rehabilitation.
DESIGN: A total of 112 maximum interference patterns of upper extremity muscles suspected of being affected by neuropathy were quantitatively evaluated based on the parameters of integration values, mean amplitudes, the number of peaks, and activity. "Activity" was defined as the sum of the time during which myoelectric signals were recorded during 1 sec with maximum voluntary contraction, and it was expressed as a percentage. The relationships of the previous parameters with spontaneous pathological potentials and polyphasic motor unit potentials were examined.
RESULTS: The area under the curve of the receiver operating characteristic curve for the diagnosis of neuropathy was the highest using activity (0.917). The integral value and mean amplitude were useful for the diagnosis of cases with chronic neuropathy showing slightly decreased interference patterns.
CONCLUSIONS: The quantitative evaluation of the maximal contraction interference pattern in this study was useful for the diagnosis of neuropathy.

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Year:  2020        PMID: 31335345     DOI: 10.1097/PHM.0000000000001268

Source DB:  PubMed          Journal:  Am J Phys Med Rehabil        ISSN: 0894-9115            Impact factor:   2.159


  1 in total

1.  On the establishment of reference values of clouds of electromyography interference pattern by linear regression method and percentile method and comparison of sensitivity and specificity of both methods.

Authors:  Dan Wang; Lei Liu; Ruohong Xue; Zhongming Li; Yuqi Gao; Ting Wang; Yanfang Kang; Jingjing Wang; Qiuye Yin; Najuan Li; Yanbing Han
Journal:  Front Neurol       Date:  2022-09-01       Impact factor: 4.086

  1 in total

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