| 基于信息论的盲源信号分离在呼吸信号分离中的应用研究 |
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| 引用本文： | 杨维娜,廖春明.基于信息论的盲源信号分离在呼吸信号分离中的应用研究[J].曲靖师专学报,2011(3):51-53. |
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| 作者姓名： | 杨维娜 廖春明 |
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| 作者单位： | 文山学院计科系,云南文山663000 |
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| 摘 要： | 从混合观测数据向量中恢复出不可直接观测的各个源信号是阵列处理和数据分析的典型问题,独立分量分析是解决这一类问题的新技术.基于信息论算法中主流的Fast ICA算法能够对几组不同的信号进行分离,和其他算法相比有一定优越性,能完整地分离出肌电信号中含有的呼吸信号.
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| 关 键 词： | 盲源信号分离 FastICA算法 呼吸信号检测 |
Application of Blind Source Separation to Respiratory Signal Separation Based on Information Theory |
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| Authors: | Yang Weina Liao Chunming |
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| Institution: | (Department of Computer Science,Wenshan University,Wenshan Yunnan 663000,P.R.China) |
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| Abstract: | Recovering the unobserved source signals from their mixtures is a typical problem in array processing and analysis.Independent component analysis（ICA） is a new method for solving the problem.The most common way based on information theory is Fast ICA algorithm that may separate the different signals.Compared with other mainstream blind separation algorithm,the Fast ICA algorithm has some advantages as separating respiratory signal completely from EMG signal. |
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| Keywords: | blind source separation independent component analysis fast ICA algorithm |
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