高灵敏度加速传感器检测微机械图和深度学习方法,用于帕金森病的分类
Jingyu Quan1, Hirotaka Uchitomi2, Ryo Shigeyama1
1Department of Computer Science, Tokyo Institute of Technology, Tokyo, 226-8502, Japan.
Scientific reports
|October 2, 2024
概括
高灵敏度的传感器在帕金森病患者中检测到以前无法检测到的肌肉微振动 (micro-MMG). 这些微型MMG模式为诊断帕金森病 (PD) 提供了一种新的,准确的方法.
科学领域:
- 生物医学工程 生物医学工程
- 神经学 神经学
- 传感器技术 传感器技术
背景情况:
- 帕金森病 (PD) 的诊断依赖于临床症状和成像.
- 传统的传感器无法检测到微妙的高频肌肉振动 (微机械图或微MMG).
- 高灵敏度加速传感器提供了增强的振动检测能力.
研究的目的:
- 在帕金森病患者 (PwPD) 和健康对照 (HC) 的延伸肌肉中研究高频微MMG.
- 评估微型MMG的诊断潜力,以区分PwPD与HC.
- 开发一种使用MMG数据进行PD分类的深度学习模型.
主要方法:
- 开发高灵敏度加速传感器,能够检测振动>10dB低于商业传感器.
- 测量延伸肌的低频 (MMG,<15 Hz) 和高频 (微-MMG,≥15 Hz) 振动.
- 应用深度学习模型来根据MMG和微MMG特征对PwPD和HC进行分类.
主要成果:
- 在PwPD和HC中检测到以前无法检测到的微型MMG.
- 在PwPD和HC之间微型MMG频率特征的显著差异.
- 在肌肉功率输出过程中,与HC相比,PwPD的微型MMG能量较低.
- 与HC相比,PwPD的低频MMG能量更高.
- 一个深度学习模型在使用两种MMG类型对PwPD和HC进行分类时实现了92.19%的准确性.
结论:
- 高灵敏度传感器和微MMG分析为区分PwPD和HC提供了关键信息.
- 微MMG代表了帕金森病的一个有前途的生物标志物.
- 开发的深度学习模型表明了PD非侵入性诊断系统的潜力.
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