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

Wei Zhu; Cuilian Zhao

doi:10.1515/bmt-2024-0107

Article

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

Wei Zhu and Cuilian Zhao

Published/Copyright: August 8, 2024

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From the journal Biomedical Engineering / Biomedizinische Technik Volume 69 Issue 6

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.

Keywords: fuzzy entropy; onset detection algorithm; orbicularis oris muscle; surface electromyography

Corresponding author: Cuilian Zhao, Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China, E-mail: clzhao@shu.edu.cn

Funding source: Science and Technology Commission of Shanghai Municipality

Award Identifier / Grant number: 16441909000

Funding source: Shanghai University Research Cultivation Fund

Award Identifier / Grant number: D.72-0109-22-007

Acknowledgments

The authors would like to thank Siyue Chen from Department of Oral and Craniofacial Surgery, Shanghai Ninth People’s Hospital, College of Stomatology, Shanghai JiaoTong University of Medicine, Shanghai, China for her assistance in collecting experimental data and all the volunteers who participated for contributing their time.

Research ethics: The research related to human use has complied with all the relevant national regulations, institutional policies, and in accordance with the tenets of the Helsinki Declaration, and has been approved by Science and Technology Ethics Review Committee of Shanghai University, China (Approval No: ECSHU 2021-180).
Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
Competing interests: Authors state no conflict of interest.
Research funding: This work was funded by the Shanghai University Research Cultivation Fund (grant number: D.72-0109-22-007) and the Science and Technology Commission of Shanghai Municipality (grant number: 16441909000). The funding organizations played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
Data availability: The raw data can be obtained on request from the corresponding author.

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Received: 2024-03-02

Accepted: 2024-07-18

Published Online: 2024-08-08

Published in Print: 2024-12-17

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Articles in the same Issue

https://doi.org/10.1515/bmt-2024-0107

Keywords for this article

fuzzy entropy; onset detection algorithm; orbicularis oris muscle; surface electromyography