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The assessment method of lip closure ability based on sEMG nonlinear onset detection algorithms
1Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.
Biomedizinische Technik. Biomedical Engineering
|August 8, 2024
Summary
This study introduces a novel surface electromyographic (sEMG) method for accurately measuring lip closure in mouth-breathing patients. The developed Lip Closure EMG Activity Index (LCEAI) offers a reliable quantitative assessment tool.
Area of Science:
- Biomedical Engineering
- Clinical Diagnostics
- Physiology
Background:
- Traditional diagnosis of orbicularis oris muscle function in mouth-breathing patients has limitations.
- Accurate assessment of lip closure ability is crucial for diagnosing mouth breathing.
Purpose of the Study:
- To propose a reliable and accurate surface electromyographic (sEMG) based method for quantitative assessment of lip closure ability.
- To overcome the limitations of traditional diagnostic methods for mouth-breathing patients.
Main Methods:
- Compared three nonlinear onset detection algorithms: Teager-Kaiser Energy (TKE), Sample Entropy (SampEn), and Fuzzy Entropy (FuzzyEn).
- Utilized sEMG signals from 21 volunteers (16 patients, 5 healthy subjects, aged 8-16).
- Developed the Lip Closure EMG Activity Index (LCEAI) based on the best-performing algorithm for quantitative assessment.
Main Results:
- Fuzzy Entropy demonstrated superior performance with a 93.78% lip closure identification rate.
- Fuzzy Entropy achieved the lowest average onset delay (47.50 ms), endpoint delay (73.10 ms), and time error (111.61 ms).
- Calculated LCEAI values closely correlated with the actual degree of lip closure observed in patients.
Conclusions:
- The proposed sEMG-based method provides a quantitative basis for diagnosing mouth breathing.
- This novel approach enhances the accuracy and reliability of assessing lip closure ability.
