Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.
Biomed Tech (Berl). 2024 Aug 8;69(6):597-608. doi: 10.1515/bmt-2024-0107. Print 2024 Dec 17.
To overcome the limitations of traditional diagnosis of orbicularis oris muscle function in mouth-breathing patients, this study aims to propose a surface electromyographic (sEMG) based method for reliable and accurate quantitative assessment of lip closure ability.
A total of 21 volunteers (16 patients and 5 healthy subjects, aged 8-16) were included in the study. Three nonlinear onset detection algorithms - Teager-Kaiser Energy (TKE) operator, Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn) - were compared for their ability to identify lip closure in sEMG signals. Lip Closure EMG Activity Index (LCEAI) was proposed based on the action segments detected by the best performing algorithm for the quantitative assessment of lip closure.
The results indicated that FuzzyEn had the highest lip closure identification rate at 93.78 %, the lowest average onset delay of 47.50 ms, the lowest average endpoint delay of 73.10 ms, and the minimal time error of 111.61 ms, exhibiting superior performance. The calculation results of the LCEAI closely corresponded with the actual degree of lip closure in patients.
The lip closure ability assessment method proposed in this study can provide a quantitative basis for the diagnosis of mouth breathing.
为了克服传统口呼吸患者口轮匝肌功能诊断的局限性，本研究旨在提出一种基于表面肌电图（sEMG）的方法，以可靠、准确地定量评估唇闭合能力。
本研究共纳入 21 名志愿者（16 名患者和 5 名健康受试者，年龄 8-16 岁）。比较了三种非线性起始检测算法 - Teager-Kaiser Energy（TKE）算子、样本熵（SampEn）和模糊熵（FuzzyEn）- 它们识别 sEMG 信号中唇闭合的能力。基于性能最佳的算法检测到的动作段，提出了唇闭合肌电图活动指数（LCEAI），用于定量评估唇闭合。
结果表明，FuzzyEn 的唇闭合识别率最高，为 93.78%，平均起始延迟最低，为 47.50ms，平均结束延迟最低，为 73.10ms，时间误差最小，为 111.61ms，表现出优越的性能。LCEAI 的计算结果与患者实际的唇闭合程度密切相关。
本研究提出的唇闭合能力评估方法可为口呼吸的诊断提供定量依据。
