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从手绘的阿基米德螺旋中提取和减少参数,用于临床震评估
Furrukh Khan1, Jessie Xiaoxi1, Andrew Uehlin2
1Department of Electrical and Computer Engineering, The Ohio State University, Columbus, OH, USA.
Heliyon
|August 15, 2024
概括
这项研究引入了一种新的数学方法,用于将手绘的阿基米德螺旋转换成用于震分析的数字信号. 这种方法有效地从各种螺旋绘图格式中提取有价值的震动数据.
科学领域:
- 神经学 神经学
- 数字信号处理 数字信号处理
- 医学成像分析 医学成像分析
背景情况:
- 手绘的阿基米德螺旋对于评估神经病患者的震至关重要.
- 传统的螺旋数字化方法 (纸张或平板电脑) 引入了文物,使得信号提取复杂化.
- 现有的方法难以应对手绘螺旋数据中固有的噪音和变化.
研究的目的:
- 开发一种新的数学绘图,用于将患者绘制的螺旋图像转换为可用的一维离散信号.
- 从螺旋图纸中对震动特征进行强大的数学分析.
- 创建一种适用于扫描纸张图纸和数字平板电脑输入的方法.
主要方法:
- 开发了一个数学映射算法,将手绘螺旋形状的图像像素转换为离散信号.
- 该方法在一组18个手绘螺旋体上得到了验证,这些螺旋体展示了各种艺术品.
- 提取信号参数并分析数据表示效率.
主要成果:
- 这种新的方法成功地将螺旋图像转化为一维的离散信号.
- 螺旋信号可以使用最少150个参数准确地表示,平均RMS误差为6.26%.
- 提取的信号特征有助于震动频率和振幅的评估,使机器学习应用成为可能.
结论:
- 一个经过验证的,最少的侵入性手术自动提取患者绘制的螺旋,作为离散的数学信号.
- 该方法对扫描的纸质文件和数字平板电脑文件有效,可以容纳现有的数据格式.
- 这种技术增强了在临床环境中自动化分析和机器学习的潜力.
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