The assessment method of lip closure ability based on sEMG nonlinear onset detection algorithms

Abstract

Abstract Objectives 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. Methods 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. Results 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. Conclusions The lip closure ability assessment method proposed in this study can provide a quantitative basis for the diagnosis of mouth breathing.