通过使用基于信号改进方法的近红外光谱技术进行补偿运动检测
Xiang Chen1, YinJin Shao2, LinFeng Zou1
1Department of Rehabilitation Medicine, The Second Affiliated Hospital of Nanchang University, Nanchang, China.
Frontiers in neuroscience
|May 26, 2023
概括
这项研究引入了一种使用近红外光谱学 (NIRS) 来检测中风幸存者的补偿运动的新方法,提高了康复的准确性. 基于NIRS的方法提高了检测性能,以获得更好的患者康复结果.
科学领域:
- 生物医学工程 生物医学工程
- 神经康复疗法 神经康复疗法
- 信号处理 信号处理
背景情况:
- 在中风幸存者中患有半的补偿运动很常见,阻碍了恢复.
- 准确检测这些运动对于有效的康复策略至关重要.
研究的目的:
- 通过近红外光谱学 (NIRS) 提出和验证一种用于检测补偿运动的新方法.
- 通过基于差异的信号改进 (DBSI) 方法来提高NIRS信号质量.
- 评估用于补偿运动检测的机器学习算法的可行性.
主要方法:
- 在康复任务期间,NIRS传感器记录了10名健康受试者和6名中风幸存者的干部肌肉激活.
- 基于差异的信号改进 (DBSI) 方法应用于预处理NIRS信号.
- 支持矢量机 (SVM) 算法分析了时间域特征 (平均值,方差) 以检测补偿行为.
主要成果:
- NIRS信号在检测补偿运动方面表现出很高的准确性 (97.76%的健康人,97.95%的中风幸存者).
- DBSI方法进一步提高了检测准确度,达到98.52% (健康) 和99.47% (中风幸存者).
- 与现有技术相比,拟议的基于NIRS的方法显示了优越的分类性能.
结论:
- 结合DBSI和机器学习的NIRS技术,为中风康复中补偿运动检测提供了一个有希望的,准确的方法.
- 这种方法在改善中风恢复结果方面具有显著的潜力,并值得进一步研究.
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