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小波域变步长ICA算法提取胎儿心电信号 被引量:1

Fetal ECG Extraction by Variable Step-size ICA Algorithm in Wavelet Domain
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摘要 胎儿心电信号是反映胎儿宫内生理活动的一种最主要的客观指标,从母体体表混合信号中分离出胎儿心电信号是近年来信号处理领域研究的热点问题。该文利用小波变换将采集到的各导联混合信号变换到小波域;在小波域结合模拟退火法变步长进行独立分量的提取;将提取到的各独立分量进行小波逆变换,从中选择胎儿心电信号成分。通过对临床实际数据进行实验,实验结果提取出了清晰的胎儿心电信号。 Fetal ECG ( Electrocardiogram ) is a primary objective indicator which reflects fetal physiological activity.Separating fetal ECG from mixed signal collected from the maternal body surface is a hot issue in signal processing field . In this paper , we transform mixed signal to wavelet domain and then combine simulated annealing method with ICA to extract independent components ;finally we transform these extracted inversely and choose the FECG component .Actual data experimental results verify the superior performance over conventional ICA algorithm .
出处 《杭州电子科技大学学报(自然科学版)》 2014年第1期25-29,共5页 Journal of Hangzhou Dianzi University:Natural Sciences
基金 浙江省科技计划公益技术研究资助项目(2012C21005)
关键词 胎儿心电信号提取 小波变换 独立分离提取 fetal ECG extraction wavelet transform simulated annealing
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参考文献6

  • 1徐进.胎儿心电的提取方法分析[J].电子工程师,2006,32(6):77-80. 被引量:5
  • 2Widrow B, Jr Glover J R, Mccool J M, et al. Adaptive noise cancelling: Principles and applications [ J]. Proceedings of the IEEE,1975,63(12) :1 692 - 1 716.
  • 3Kanjilal P P, Palit S, Saha G. Fetal ECG extraction from single-channel maternal ECG using singularvalue decomposition[ J ]. IEEE Transactions on Biomedical Engineering, 1997,44 ( 1 ) :51 - 59.
  • 4Assaleh K, A1-Nashash H. A novel technique for the extraction of fetal ECG using polynomial networks [ J ]. IEEE Transactions on Biomedical Engineering,2005,52(6) :1 148 -1 152.
  • 5Mcsharry P E, Clifford G D, tarassenko L, et al. A dynamical model for generating synthetic electrocardiogram signals [ J ]. IEEE Transactions on Biomedical Engineering,2003,50(3) :289 -294.
  • 6高颖,李月,杨宝俊.变步长自适应盲源分离算法综述[J].计算机工程与应用,2007,43(19):75-79. 被引量:10

二级参考文献30

  • 1李晓欧,张笑微,冯焕清.一种新的变步长ICA自适应算法[J].电路与系统学报,2004,9(6):113-117. 被引量:3
  • 2孙守宇,郑君里,吴里江,赵莹.峭度自适应学习率的盲信源分离[J].电子学报,2005,33(3):473-476. 被引量:11
  • 3曾禹村,张宝俊,吴鹏翼,等.信号与系统[M].北京:北京理工大学出版社,2001.
  • 4Cichocki A,Amari S.Adaptive blind signal and image processing:learning algorithms and applications[M].New York:Wiley,2002:335-343.
  • 5Hyvarinen A,Karhunen J,Oja E.Independent Component Analysis[M].New York:Wiley,2001:165-202.
  • 6Yang H H,Amari S.Adaptive on-line learning algorithm for blind separation:maximum entropy and minimum mutual information[J].Neural Computation,1997,9(7):1457-1482.
  • 7Bell A J,Sejnowski T J.An information-maximization approach to blind separation and blind deconvolution[J].Neural Computation,1995,7(6):1129-1159.
  • 8Amari S.Natural gradient works efficiently in learning[J].Neural Computation,1998,10 (2):251-276.
  • 9Amari S,Douglas S C.Why natural gradient[C]//IEEE International Conference on Acoustics,Speech,and Signal Processing,1998,2:1213-1216.
  • 10Cardoso J F,Donoho D L.Equivariant adaptive source separation[J].IEEE Trans on Signal Processing,1996,44(12):3017-3029.

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