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北京大学学报（医学版） ›› 2026, Vol. 58 ›› Issue (1): 89-98. doi: 10.19723/j.issn.1671-167X.2026.01.012

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单侧磨牙咬合干扰的口颌肌表面肌电指标体系构建与判别模型验证

李文博1,*, 沈玉凤2,*, 杨咏涛1, 单珅瑶1, 高梓翔3, 温奥楠3, 商相宜1, 田淯文1, 郭殊玮3, 王艺蓁1, 王勇3,*(), 赵一姣1,*()   

  1. 1. 北京大学医学部医学技术研究院, 北京 100191
    2. 石河子大学第一附属医院口腔科, 新疆石河子 832008
    3. 北京大学口腔医学院·口腔医院口腔医学数字化研究中心, 国家口腔医学中心, 国家口腔疾病临床医学研究中心, 口腔生物材料和数字诊疗装备国家工程研究中心, 国家卫生健康委口腔数字医学重点实验室, 口腔数字医学北京市重点实验室, 北京 100081
  • 收稿日期:2025-10-16 出版日期:2026-02-18 发布日期:2025-12-17
  • 通讯作者: 王勇, 赵一姣
  • 作者简介:

    *These authors contributed equally to this work

  • 基金资助:
    北京市自然科学基金-海淀原始创新联合基金(L232100); 北京市自然科学基金-海淀原始创新联合基金(L242132); 国家自然科学基金(82271039); 八师石河子市财政科技计划中青年科技创新骨干人才项目(2025RC04); 兵团科技计划青年科学项目(2025DB040); 国家重点研发计划项目(2022YFC2405401)

Development of a surface electromyography index system for orofacial muscles and validation of a discriminant model in unilateral molar occlusal interference

Wenbo LI1, Yufeng SHEN2, Yongtao YANG1, Shenyao SHAN1, Zixiang GAO3, Aonan WEN3, Xiangyi SHANG1, Yuwen TIAN1, Shuwei GUO3, Yizhen WANG1, Yong WANG3,*(), Yijiao ZHAO1,*()   

  1. 1. Institute of Medical Technology, Peking University Health Science Center, Beijing 100191, China
    2. Department of Stomatology, The First Affiliated Hospital of Shihezi University, Shihezi 832008, Xinjiang, China
    3. Center for Digital Dentistry, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & NHC Key Laboratory of Digital Stomatology & Beijing Key Laboratory of Digital Stomatology, Beijing 100081, China
  • Received:2025-10-16 Online:2026-02-18 Published:2025-12-17
  • Contact: Yong WANG, Yijiao ZHAO
  • Supported by:
    the Beijing Natural Science Foundation(L232100); the Beijing Natural Science Foundation(L242132); the National Natural Science Foundation of China(82271039); the Young and Middle-Aged Backbone Talent in Science and Technology Innovation Project under the Shihezi City (Eighth Division) Financial Science and Technology Plan(2025RC04); the Youth Science Project under the Xinjiang Production and Construction Corps Science and Technology Plan(2025DB040); the National Key Research and Development Program of China(2022YFC2405401)

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摘要：

目的: 构建标准化的单侧磨牙咬合干扰模型, 建立基于表面肌电的口颌肌功能指标体系, 并开发咬合干扰的判别模型, 为咬合干扰的客观诊断提供电生理学参考。方法: 招募到26名志愿者, 采用口内扫描、计算机辅助设计及增材制造技术制作标准化咬合干扰贴片, 于下颌第一磨牙处构建可逆性单侧咬合干扰模型。使用自主研发的多通道无线表面肌电系统采集干扰前后10种下颌功能活动时双侧颞肌前束、咬肌及二腹肌前腹的肌电信号, 构建包含56项指标的多维度表面肌电指标体系。通过配对t检验或Wilcoxon符号秩检验、主成分分析和Logistic回归分析, 筛选特征变量并建立判别模型。结果: 共构建40例有效干扰模型, 253项表面肌电信号指标干扰前后比较差异具有统计学意义(P < 0.05), 其中侧方运动相关指标对咬合干扰的敏感性最高。主成分分析共提取19个主成分(principal component, PC), 累计方差贡献率85.5%, 其中PC1(肌肉疲劳程度)和PC2(功能性运动幅度)为主要解释成分。Logistic回归模型最终纳入3项主成分, 交叉验证结果表明模型平均准确率为0.840, 平均灵敏度与特异度分别为0.851和0.828, 平均曲线下面积为0.923。结论: 研究构建的单侧磨牙咬合干扰Logistic回归判别模型能有效识别本实验条件下的咬合干扰状态, 具有较好的诊断潜力。

关键词: 咬合干扰, 表面肌电, 口颌肌, 回归模型

Abstract:

Objective: To construct a standardized unilateral molar occlusal interference model, to establish a comprehensive surface electromyography (sEMG)-based index system for orofacial muscle function, and to develop an accurate discriminant model, thereby providing an objective electrophysiological basis for occlusal interference diagnosis. Methods: Twenty-six healthy adult volunteers were recruited and provided written informed consent. Utilizing advanced digital dental technology, including intraoral scanning, computer-aided design (CAD), and additive manufacturing, a standardized occlusal inter-ference patch with a precise thickness was fabricated. This patch was adhesively bonded to the occlusal surface of the mandibular first molar to create a reversible unilateral occlusal interference model. A self-developed, multi-channel wireless sEMG system was employed to collect high-fidelity electromyographic signals from key bilateral masticatory muscles: the anterior temporal muscles, masseters, and the anterior bellies of the digastric muscles. Data were recorded during 10 standardized mandibular functional activities both before (baseline) and after the induction of interference. From the raw sEMG signals, a multi-dimensional index system comprising 56 distinct indicators across time, frequency, and complexity domains was constructed. Sophisticated statistical analyses, including paired-sample t-tests (or Wilcoxon signed-rank tests), principal component analysis (PCA) for dimensionality reduction, and stepwise Logistic regression analysis, were applied to screen for the most significant feature variables and to build the optimal discriminant model. Results: Forty valid interference models were successfully established. Statistical analysis revealed 253 sEMG indicators showed significant differences following interference induction (P < 0.05), with lateral-movement-related parameters demonstrating particular sensitivity. PCA extracted 19 principal components (PCs) explaining 85.5% of cumulative variance, where PC1 (muscle fatigue level) and PC2 (functional movement amplitude) represented the primary explanatory components. The optimal Logistic regression model incorporated 3 principal components. Cross-validation showed the model achieved a mean accuracy of 0.840, with mean sensitivity and specificity of 0.851 and 0.828, respectively, and a mean area under the curve (AUC) of 0.923. Conclusion: The Logistic regression discriminant model for unilateral molar occlusal interference constructed in this study can effectively identify the occlusal interference state under the experimental conditions, demonstrating promising diagnostic potential.

Key words: Occlusal interference, Surface electromyography, Orofacial muscles, Regression model

中图分类号: 

  • R78

图1

标准化咬合干扰贴片设计制作流程"

表1

本研究构建的口颌面肌肉sEMG指标体系"

Category Indicator name Data source Formula
Common sEMG indicators for muscle function assessment in oral medicine Percentage overlapping coeffificient，POC Normalized RMS data $\frac{1}{n}\sum\limits_{i = 1}^n {\left( {1 - \frac{{NI{S_i} - I{S_i}}}{{NI{S_i} + I{S_i}}}} \right)} \times 100\% {\rm{ }} $
Modified asymme- try index，mAI Normalized RMS data $ \frac{1}{n}\sum\limits_{i = 1}^n {\frac{{M{M_{NIS - i}} + T{A_{NIS - i}} + D{A_{NIS - i}} - M{M_{IS - i}} - T{A_{IS - i}} - D{A_{IS - i}}}}{{M{M_{NIS - i}} + T{A_{NIS - i}} + D{A_{NIS - i}} + M{M_{IS - i}} + T{A_{IS - i}} + D{A_{IS - i}}}}} \times 100\% $
Activity index，Ac Normalized RMS data $\frac{1}{n}\sum\limits_{i = 1}^n {\frac{{M{M_{NS - i}} + M{M_{IS - i}} - T{A_{NIS - i}} - T{A_{IS - i}}}}{{M{M_{NIS - i}} + M{M_{IS - i}} + T{A_{NIS - i}} + T{A_{IS - i}}}}} \times 100\% $
Torque coeffifi- cient，TC Normalized RMS data $ \frac{1}{n}\sum\limits_{i = 1}^n {\frac{{\left| {T{A_{NIS}} - T{A_{IS}}} \right| - \left| {M{M_{NIS}} - M{M_{IS}}} \right|}}{{\left( {M{M_{NIS}} + T{A_{NIS}} + M{M_{IS}} + T{A_{IS}}} \right)}}} \times 100\% $
Time-frequency domain sEMG indicators Average rectified value, ARV Filtered data ${\rm{ For\; channel }}j, \frac{1}{n}\sum\limits_{i = 1}^n {\left| {{x_{ij}}} \right|} $
Median frequency, MF Filtered data $ \int_0^{f_{\text {median }}} P(f) \mathrm{d} f=\frac{1}{2} \int_0^{f_{\text {max }}} P(f) \mathrm{d} f$
Mean power fre- quency, MPF Filtered data $ \frac{\int_0^{f_{\max }} f \cdot P(f) \mathrm{d} f}{\int_0^{f_{\max }} P(f) \mathrm{d} f}$
Novel sEMG indicators Coefficient of variation, CV Normalized RMS data $\begin{array}{*{20}{c}}{{\rm{For \;channel }}j{\rm{, }}}\\{C{V_j} = \frac{{\sqrt {\frac{1}{{n - 1}}\sum\limits_{i = 1}^n {{{\left( {{x_{ij}} - {{\bar x}_j}} \right)}^2}} } }}{{\frac{1}{n}\sum\limits_{i = 1}^n {{x_{ij}}} }} \times 100\% }\end{array} $
Muscle synergy in- dex, MSI Normalized RMS data Calculation of the Pearson correlation matrix from six-channel RMS data, extraction of the fifteen off-diagonal elements from its upper triangular part, and computation of the arithmetic mean of their absolute values.

图2

各下颌动作中干扰前后差异有统计学意义的sEMG指标数量分布图"

图3

口颌面肌群sEMG指标主成分分析碎石图及累积方差贡献率分布图"

表2

主成分代表性指标载荷及特征归纳"

Variable Primary feature High-loading indicators Loading
PC1 Muscle fatigue level MF_IS (mouth opening-closing movement) 0.70
MF_mean (mouth opening-closing movement) 0.67
MF_IS-DA (IS chewing) 0.66
MF_DA (IS lateral movement) 0.65
PC2 Functional movement amplitude ARV_NIS-TA (mouth opening-closing movement) 0.78
ARV_IS-MM (IS lateral movement) 0.77
ARV_NIS-MM (NIS lateral movement) 0.77
ARV_NIS-TA (protrusive movement) 0.74

图4

干扰组与非干扰组在主成分空间的得分散点图"

表3

纳入Logistic回归模型的主成分特征及代表性变量"

Principal component Primary feature High-loading indicators Loading
PC2 Functional movement amplitude ARV_NIS-TA (mouth opening-closing movement) 0.78
ARV_IS-MM (IS lateral movement) 0.77
ARV_NIS-MM (NIS lateral movement) 0.77
ARV_NIS-TA (protrusive movement) 0.74
PC8 Static activity stability MF_NIS-DA (rest position) 0.58
ARV_NIS-DA (clenching) 0.48
CV_IS (rest position) 0.41
PC9 Fine-motor control CV_MM (ICP light contact) 0.68
CV_IS-MM (ICP light contact) 0.59
MF_IS-DA (rest position) 0.49

图5

基于主成分的Logistic回归分析结果"

图6

本研究构建的Logistic回归模型交叉验证性能指标箱线图"

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ISSN 1671-167X
CN 11-4691/R
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