期刊文献+

基于回归支持向量机的胎儿心电提取 被引量:4

Extraction of Fetal Electrocardiogram Using Support Vector Regression Machine
下载PDF
导出
摘要 针对胎儿心电难以提取的问题,提出一种从母体腹壁混合信号中提取胎儿心电的方法。首先利用回归支持向量机(Support vector regression machine,SVRM)拟合母体心电传导至腹壁所经历的非线性变换,然后将母体心电经由所拟合的非线性变换得到腹壁混合信号中的母体心电干扰的最优估计,再从腹壁混合信号中减去母体心电干扰的最优估计得到含噪声的胎儿心电,最后通过小波包去噪技术抑制胎儿心电中的基线漂移和噪声,得到清晰的胎儿心电。在胎儿心电和母体心电QRS波完全重叠的情况下,通过该方法能够提取出清晰的胎儿心电。实验结果验证了该方法的有效性。 A novel method based on support vector regression machine (SVRM) is proposed to extract the fetal electrocardiogram (FECG) from the abdominal composite signal of the pregnant woman. The maternal electro cardiogram (MECG) interference in the abdominal composite signal is a nonlinearly transformed version of the MECG and this nonlinearly relation is identified by the SVRM. An optimal estimation of the MECG interference in the abdominal composite signal is obtained by the MECG undergoing the nonlinear transformation. The fetal electrocardiogram(FECG) is extracted by removing the optimal estimation of the MECG interference from the abdominal composite signal. The baseline shift and the noise in the extracted FECG are suppressed by the wavelet packet denoising technique. Experimental results show that the clear FECG can be extracted even under the condition of the fetal QRS wave being entirely overlapped with the maternal QRS wave in the abdominal composite signal. Experimental results verify the proposed method.
出处 《数据采集与处理》 CSCD 北大核心 2009年第6期738-743,共6页 Journal of Data Acquisition and Processing
基金 重庆市自然科学基金(2007BB2150 2008BB2332)资助项目
关键词 胎儿心电 非线性变换 回归支持向量机 小波包去噪 fetal electrocardiogram nonlinear transformation support vector regression machine wavelet packet denoising
  • 相关文献

参考文献11

  • 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.

同被引文献43

引证文献4

二级引证文献17

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