从振动声学信号中检测软骨损伤的混合框架,使用集体实证模式分解和CNNs
Anna Machrowska1, Robert Karpiński1,2, Marcin Maciejewski3
1Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland.
Sensors (Basel, Switzerland)
|November 13, 2025
概括
这项研究引入了一个混合框架,使用振动声学信号来检测膝关节骨关节炎 (OA) 和肌肉病. 该方法结合了信号分析和深度学习,用于准确,非侵入性地诊断软骨退化.
科学领域:
- 生物力学和生物医学工程
- 医疗信号处理 医疗信号处理
- 计算病理学计算病理学
背景情况:
- 膝关节骨关节炎 (OA) 是一种普遍存在的退行性关节疾病.
- 早期诊断冠状腺癌,这是OA的一个关键特征,对于有效的管理至关重要.
- 目前的诊断方法可能具有侵入性或缺乏对早期软骨损伤的敏感性.
研究的目的:
- 开发和验证一种混合分析框架,用于使用振动声学 (VAG) 信号进行非侵入性检测.
- 为了比较不同机器学习模型 (SVM和CNN) 在分类膝关节OA方面的表现.
- 评估VAG信号与先进的信号处理和深度学习相结合的潜力,用于早期膝盖病理诊断.
主要方法:
- 震动声学 (VAG) 信号从膝关节骨关节炎 (OA) 患者和健康对照 (HCs) 在膝关节曲延伸 (开放和关闭的动力链) 期间获得.
- 非线性信号分解 (集体实证模式分解 (EEMD)) 和波动分析 (延迟波动分析 (DFA)) 应用于VAG信号.
- 使用支持矢量机器 (SVM) 进行了特征提取,选择 (邻近组件分析 - NCA) 和分类.
- 卷积神经网络 (CNN) 被用来分类来自VAG信号的连续波波变换 (CWT) 卷积图.
主要成果:
- 在封闭的动力链 (CKC) 条件下,SVM方法实现了高性能,精度为0.87,曲线下的面积 (AUC) 为0.91.
- 美国有线电视新闻网对CWT图的分类表明,OA患者和健康对照者之间存在强烈的歧视.
- 混合框架成功识别了VAG信号导致软骨退化的特征.
结论:
- 拟议的混合框架结合了多尺度分解,非线性波动分析和深度学习,为检测软骨退化提供了准确且非侵入性的方法.
- 振动声学信号分析对膝关节关节炎和相关病理的早期诊断具有重大潜力.
- 这些发现支持VAG信号作为风湿病学和骨科诊断工具的临床实用性.
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