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在分类神经退行性疾病时,同时对两只腿的步态信号进行时间频率分析

Farhad Abedinzadeh Torghabeh¹, Elham Ahmadi Moghadam¹, Seyyed Abed Hosseini²

¹Department of Biomedical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

|August 7, 2024

概括

这项研究引入了一种用于诊断神经退行性疾病 (NDD) 的新方法,通过分析行走过程中四肢相互作用. 该方法使用波形连贯性和深度学习,在非侵入性地分类NDD中实现高精度.

科学领域:

生物医学工程生物医学工程
计算神经科学是一种神经科学.
医学诊断医学诊断医学诊断

背景情况:

神经退行性疾病 (NDD) 存在重大诊断挑战.
早期和准确的诊断对于有效的患者管理至关重要.
使用可穿戴传感器的步态分析显示了在NDD中检测运动障碍的潜力.

研究的目的:

评估肢体相互作用的时间频域分析对NDD分类的有效性.
为了评估深度转移学习模型的性能,使用颜色编码的步态图像进行NDD分类.

主要方法:

利用了GaitNDD数据库,包括亨廷顿病,ALS,帕金森病和对照病患者.
应用信号准备,波形连贯性和主要组件分析用于特征提取.
采用深度转移学习模型 (AlexNet,GoogLeNet,SqueezeNet) 来进行五倍交叉验证的分类.

主要成果:

分类准确度随时间窗口的长度而变化,在AlexNet的10秒段中达到99.20%.
谷歌LeNet和SqueezeNet也表现出高准确度,超过95%的10秒段.
统计分析证实了提取的步态特征的歧视力.

结论:

拟议的非侵入性方法,结合波形连贯性和深度学习,在自动化NDD诊断中实现了卓越的性能.
这种方法为传统诊断方法提供了成本效益高的替代方案.
通过分析四肢间行走动态,成功地证明了NDD的准确分类.

关键词:

深度学习是一种深度学习.路径信号信号的路径信号神经退行性疾病的神经退行性疾病时间频率分析.

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