期刊文献+

使用时频盲源分离和小波包去噪的胎儿心电信号提取 被引量:1

Extraction of fetal electrocardiogram signal utilizing blind source separation based on time-frequency distributions and wavelet packet denoising
下载PDF
导出
摘要 提出一种使用时频盲源分离(TFBSS)和小波包去噪的胎儿心电信号提取新方法。首先通过重排时频谱时频盲源分离方法进行胎儿心电信号的初次提取,并将初次提取得到的母体心电信号和噪声对应的各路分量置零,其余分量由混合矩阵进行重构;然后再利用重排时频谱的时频盲源分离方法对重构信号进行胎儿心电信号的二次提取,得到含噪声的胎儿心电信号;最后通过小波包去噪抑制胎儿心电信号中的基线漂移和噪声。在胎儿心电信号和母体心电信号的QRS波无重叠、部分重叠或完全重叠的情况下,通过该方法能有效抑制母体心电信号和噪声的干扰,提取胎儿心电信号。实验结果表明该方法能提取清晰的胎儿心电信号。 A new method utilizing Blind Source Separation based on Time-Frequency distributions(TFBSS) and wavelet packet denosing was proposed to extract the Fetal ElectroCardioGram(FECG) signal.The original eight ElectroCardioGram(ECG) signals obtained from the thoracic and abdominal area of the pregnant woman were firstly processed to eight components by utilizing rearrangement TFBSS.Then the Maternal ECG(MECG) signal and noise components in eight components were set by zero and the rest components were reconstructed by using the mixing matrix.The FECG with noise could be extracted by separating the reconstructed result by using rearrangement TFBSS.Finally,the baseline shift and noise in FECG were suppressed by wavelet packet denoising technique.The FECG could be extracted even under the condition of the fetal QRS wave being partly and entirely overlapped with the maternal QRS wave in the abdominal composite signal.The experimental results show that the clear FECG can be extracted by utilizing the proposed method.
作者 韩亮 蒲秀娟
出处 《计算机应用》 CSCD 北大核心 2013年第8期2394-2396,2400,共4页 journal of Computer Applications
基金 国家自然科学基金资助项目(61171158) 重庆市自然科学基金资助项目(cstc2012jjA10128) 中央高校基本科研业务费资助项目(CDJZR10160002)
关键词 胎儿心电信号 时频盲源分离 小波包去噪 母体心电信号 重排时频谱 Fetal ElectroCardioGram(FECG) Blind Source Separation based on Time-Frequency distributions(TFBSS) wavelet packet denoising Maternal ElectroCardioGram(MECG) rearrangement time-frequency distributions
  • 相关文献

参考文献14

  • 1RUBEN M C, JOSE L C O, SUSANA H M, et al. Fast technique for noninvasive fetal ECG extraction [ J]. IEEE Transactions on Bio- medical Engineering, 2011, 58(2): 227-230.
  • 2LIU S J, LIU D L, ZHANG J Q, et al. Extraction of fetal electro- cardiogram using recursive least squares and normalized least mean squares algorithms [ C]//3rd Intemational Conference on Advanced Computer Control. Piscataway: IEEE, 2011:333-336.
  • 3CAMPS-VALLS G, MARTINEZ-SOBER M, SORIA-OLIVAS E, et al. Foetal ECG recovery using dynamic neural networks [ J]. Artifi- cial Intelligence in Medicine, 2004, 31(3): 197 -209.
  • 4蒲秀娟,曾孝平,韩亮,程军.基于回归支持向量机的胎儿心电提取[J].数据采集与处理,2009,24(6):738-743. 被引量:4
  • 5高莉,黄力宇.基于自适应梯度盲源分离算法的胎儿心电提取[J].仪器仪表学报,2008,29(8):1756-1760. 被引量:12
  • 6CAMARGO-OLIVARES J L, MARTIN-CLEMENTE R. The mater- hal abdominal ECG as input to MICA in the fetal ECG extraction problem [J]. IEEE Signal Processing Letters, 2011, 18(3) : 161 - 164.
  • 7VAPNIK V. An Overview of Statistical Learning Theory[ J]. IEEE Transaction on Neural Networks, 1999, 10(5) : 988 -999.
  • 8OUTRAM N J. Intelligent pattern analysis of the foetal eleetroeardio- gram [D]. Plymouth: University of Plymouth, 1997.
  • 9BELOUCHRANI A, AMIN M G. Blind source separation based on time-frequency signal representations [ J]. IEEE Transaction on Signal Processing, 1998, 46(11) : 2888 - 2897.
  • 10HOLOBAR A, FEVOTTE C, DONCARLI C, et al. Single autote- rms selection for blind source separation in time-frequency plane [ C]//EUSIPCO 2002: Proceedings of the 1 hh European Signal Processing Conference. Toulouse: [ s. n. ], 2002:1 -4.

二级参考文献29

  • 1Sato M, Kimura Y, Chida S, et al. A novel extraction method of fetal electrocardiogram from the composite abdominal signal[J]. IEEE Tran Biomed Eng, 2007, 54(1): 49-58.
  • 2Nazarpour K, Ebadi S, Sanei S. Fetal electrocardiogram signal modelling using genetic algorithm [C]//Proc IEEE MeMeA 2007. Warsaw, Poland: IEEE, 2007: 1-4.
  • 3Vicente Z, Nandi A K. Noninvasive fetal electrocardiogram extraction: blind separation versus adaptive noise cancellation [J]. IEEE Trans Biomed Eng, 2001, 48(1): 12-18.
  • 4Assaleh K, AI-Nashash H. A novel technique for the extraction of fetal ECG using polynomial networks[J]. IEEE Trans Biomed Eng, 2005, 52(6): 1148-1152.
  • 5Vapnik V. An overview of statistical learning theory[J]. IEEE Trans Neural Networks, 1999, 10(5):988-999.
  • 6Vapnik V, Golowich S, Smola A. Support vector method for function approximation, regression estimation, and signal processing [M]. Cambridge, MA:MIT Press, 1997:281-287.
  • 7Lee M S, Keerthi S S, Ong C J, et al. An efficient method for computing leave-one-out error in support vector machines with gaussian kernels [J]. IEEE Trans Neural Networks, 2004, 15(3): 750-757.
  • 8Lathauwer L. Database for the identification of systerns : FECG data EAST/SISTA K.U. Leuven, Belgium [EB/OL]. [2006-02-14]. http://www, esat. kuleuven, ae. be/sista/daisy/.
  • 9Wu Y, Tang Z, Xu Y, et al. Support vector regression for measuring electromagnetic parameters of magnetic thin-film materials [J]. IEEE Trans on Magnetics, 2007, 43(12):4071-4075.
  • 10Outram N J. Intelligent pattern analysis of the foetal electrocardiogram[D]. Plymouth, UK:Univ of Plymouth, 1997.

共引文献13

同被引文献12

引证文献1

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部