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先进的信号处理和机器学习方法用于分析和分类膝关节骨关节炎的振动动脉图信号
Vikas Kumar1, Pooja Kumari Jha2, Manoj Kumar Parida1
1School of Electronics Engineering, Kalinga Institute of Industrial Technology, Bhubaneswar, Odisha 751024, India.
Medical engineering & physics
|April 3, 2025
概括
这项研究引入了振动关节学 (VAG),用于非侵入性膝关节关节炎 (KOA) 检测. 先进的信号处理和集群精确地分级KOA,提供了一个有前途的诊断工具.
科学领域:
- 生物医学工程 生物医学工程
- 医学诊断 医学诊断 医学诊断
- 信号处理 信号处理
背景情况:
- 骨关节炎 (OA) 是导致老年人残疾的主要原因,导致疼痛和功能限制.
- 目前的诊断方法如X射线,CT和MRI在早期检测和分级方面存在局限性.
- 需要用于早期膝关节骨关节炎 (KOA) 诊断的非侵入性,具有成本效益的技术.
研究的目的:
- 提出和验证一种新的振动关节学 (VAG) 技术,用于早期检测和分类膝关节关节炎 (KOA).
- 评估使用VAG信号进行KOA分类的先进信号处理和机器学习算法的有效性.
主要方法:
- 采集了KOA患者的VAG信号,使用数字耳语仪和膝盖支架 (20 Hz2000 Hz).
- 应用多种信号处理技术,包括时间域,统计分析,PSD,波形和希尔伯特-黄变形.
- 利用自组织地图 (SOMs) 和K-means集群的新组合用于KOA等级细分.
主要成果:
- 清晰的时间域模式和SD/平均比率与OA严重程度相关.
- 希尔伯特-黄转换确定了与OA阶段相关的内在模式函数.
- 波形和光谱分析显示,随着疾病的进展,信号复杂性增加.
- 集群模型实现了高准确度,由约0.80的轮系数和约0.33.3的戴维斯-博尔丁指数表示.
结论:
- 振动关节学 (VAG) 显示了对膝关节骨关节炎 (KOA) 的非侵入性,早期检测和分级的显著潜力.
- 先进的信号处理与SOMs和K-means集群相结合,有效地分类KOA阶段.
- 这种方法为医学诊断提供了有前途的工具,特别是对于需要早期检测和监测的慢性疾病,如KOA.
相关概念视频
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