对面部表情肌肉中的电信号进行比较分析
Luna Adamov1, Bojan Petrović1, Lazar Milić2
1Faculty of Medicine, University of Novi Sad, Dr Zorana Djindjica 1, 21000, Novi Sad, Serbia.
Biomedical engineering online
|February 12, 2025
概括
表面电肌图 (SEMG) 有效评估面部肌肉功能,有助于诊断和治疗口腔运动障碍. 这项研究表明SEMGG.
科学领域:
- 面部或面部肌肉功能评估.
- 生物医学工程在诊断中的应用.
背景情况:
- 面部表情肌肉对面部系统的健康至关重要,影响言语,和吞.
- 了解肌肉功能是整体健康的关键.
研究的目的:
- 评估表面电肌图 (SEMG) 以评估健康个体的面部肌肉健康和功能.
- 探索模式识别和人工智能来区分肌肉任务.
主要方法:
- 在24名参与者中,在5种面部表情中检查了三个面部肌肉 (m. Orbicularis Oris,m. Zygomaticus Major,m. Mentalis).
- 从电肌图表中提取特征,并使用后勤回归,随机森林和线性差异分析进行分类.
- 分析了肌肉活动的时间和频率域特征.
主要成果:
- 显示了面部肌肉之间肌肉活动幅度的统计学上显著差异.
- 使用时间和频率域特征显示了显著的差异化能力.
- 模式识别技术表明了未来研究和开发的有希望的潜力.
结论:
- 表面电肌图 (SEMG) 是评估面部表情肌肉功能的一个有价值的工具.
- SEMG在诊断和治疗口腔运动功能障碍方面做出了重大贡献.
- 该研究强调了该领域进一步研究和开发的潜力.
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