通过机器学习数据驱动的中风识别,应用于多模电动图和动力学中的复杂度指标
Francesco Romano1, Damiano Formenti2, Daniela Cardone1
1Department of Engineering and Geology, University G. D'Annunzio of Chieti-Pescara, 65127 Pescara, Italy.
Entropy (Basel, Switzerland)
|July 26, 2024
概括
机器学习使用步态分析准确地区分中风幸存者和健康个体. 这种方法分析肌肉激活和运动模式,可以帮助监测中风后的运动恢复.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 康复科学 康复科学 康复科学
背景情况:
- 卒中会导致大脑损伤,往往导致需要康复的步行障碍.
- 电肌图 (EMG) 和立体相图测量评估在行走期间的肌肉激活和运动.
- 电磁场和动力学的复杂度指标可以识别病态步行模式.
研究的目的:
- 用机器学习将中风患者与健康人群区分开来.
- 用EMG和动力学数据分析步态复杂性.
- 为了评估机器学习分类器对中风步态评估的有效性.
主要方法:
- 在行走过程中收集EMG和动力学数据.
- 应用复杂度指标 (样本,近似,光谱) 到EMG和动力学.
- 使用机器学习分类器,包括立方支向量机,用于分类.
- 计算了EMG和动力学之间的条件,以分析关系.
主要成果:
- 机器学习分类器有效地区分了中风患者和健康对照.
- 应用到EMG复杂度指标的立方支向量机实现了99.85%的准确性.
- 步态的复杂性测量,肌肉激活和它们的相互关系提供了重要的歧视性信息.
结论:
- 来自EMG和动力学的步态复杂度指标的机器学习分析是用于中风检测的高度准确的方法.
- 这种方法显示出对中风患者运动恢复的客观监测有希望.
- 这些发现支持将先进的计算方法集成到用于中风康复的临床步态分析中.
关键词:
在EMGEMGEMGEMGEMGEMGEMGEMGEMGEM大致的是大致的.复杂性分析分析复杂性分析有条件的.步态分析 步态分析缺血性中风 中风机器学习是机器学习.运动功能障碍 运动功能障碍样本的度是什么样子它的光谱是光谱.更多相关视频
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