肌电图
肌肉张力
物理医学与康复
电流(流体)
语音识别
心理学
医学
计算机科学
工程类
电气工程
作者
Feifan Wang,Edwin M.‐L. Yiu
出处
期刊:Journal of Voice
[Elsevier BV]
日期:2021-12-10
卷期号:38 (3): 800.e1-800.e12
被引量:11
标识
DOI:10.1016/j.jvoice.2021.10.006
摘要
This integrative review aims to determine the quality level of evidence on using surface electromyography (sEMG) as a diagnostic tool in identifying muscle tension dysphonia.Two independent reviewers used one search engine and five databases to identify sEMG studies published between January 1980 and December 2020, using a set of specified search terms related to muscle tension dysphonia. The selected articles were systematically evaluated by two independent raters using a modified critical appraisal of diagnostic evidence (m-CADE) form.Nine articles that satisfied the inclusion criteria were selected from among 576 studies for evaluation. These nine studies showed varied methodological approaches in sEMG measurements, including electrode configuration and position, tasks used in sEMG data collection, outcome measure, and normalization procedures. Five studies showed relatively high m-CADE scores, which were indicative of "suggestive validity and compelling importance". Two studies were rated as "suggestive validity and importance", while two remaining studies were rated as "less suggestive or equivocal validity and importance".The review found a moderate level of evidence that sEMG can be a potentially useful tool with diagnostic value in identifying muscle tension dysphonia. However, evidence is not yet available to determine the diagnostic accuracy of sEMG for muscle tension dysphonia. More studies are needed, and it is recommended that future studies involving sEMG and reference measurements should be undertaken using a blinding procedure in order to control any subjective biases. Details of the population that the sEMG has been tested on should be outlined clearly so that spectrum bias could be eliminated or minimized in the application process. Furthermore, it is suggested that a reliable and valid protocol in collecting sEMG data during speech should be developed to minimize the variability of sEMG measures in assessing muscle activities during speech.
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