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基于健壮性独立分量分析的胎儿心电分离 被引量：1

Fetal Electrocardiogram Extraction Based on Robust Independent Component Analysis
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摘要 独立分量分析(ICA)旨在将观测的随机向量分解为相互独立的变量,其中快速定点算法(FastICA)及其变种以其快速简单的分离效果得到日益关注。健壮性独立分量分析(RobustICA)采用最优步长改进了FastICA的不足。最后在胎儿心电(FECG)分离方面和FastICA相比较,结果证明RobustICA优越的表现。 Independent component analysis （ICA）aims at decomposing an observed random vector into statistically in- dependent variables. Fast independent component analysis （FastICA） algorithm and its variants are catching more at tention because of their simplicity and convergence speed. In this paper, a novel method referred to robust independ- ent component analysis （RobustICA）, based on normalized kurtosis and optimal step-size, is analyzed in detail. When applied to fetal electrocardiogram （FECG） extraction and compared with FastICA, it gave decent results and showed prosperous future usages.
作者 姚文坡 王俊
机构地区 南京邮电大学通信与信息工程学院图像处理与图像通信重点实验室 南京邮电大学地理与生物信息学院图像处理与图像通信重点实验室
出处 《生物医学工程学杂志》 EI CAS CSCD 北大核心 2013年第6期1191-1194,共4页 Journal of Biomedical Engineering
基金 国家自然科学基金资助项目(61271082 61201029 61102094) 江苏省自然科学基金资助项目(BK2011759 BK2011565)
关键词 快速定点算法 胎儿心电 峭度 最优步长 健壮性独立分量分析 Fast independent component analysis （FastlCA） Fetal electrocardiogram（ FECG） Kurtosis Optimalstep size Robust independent component analysis（RobustICA）
分类号 R714.5 [医药卫生—妇产科学]
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参考文献15

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同被引文献19

  • 1谈华暠,刘海林.盲稀疏源信号分离算法的恢复性研究[J].广东工业大学学报,2007,24(3):28-31. 被引量：3
  • 2Turan S, Turan O M, Berg C, et al. Computerized fetal heart rate analysis, Doppler ultrasound and biophysical pro- file score in the prediction of acid-base status of growth - restricted fetuses [ J ]. Ultrasound in Obstetrics & Gynecolo- gy, 2007, 30 (5) : 750-756.
  • 3Graatsma E M, Jacod B C, Van Egmond L A J, et ah Fe- tal electrocardiography: feasibility of long term fetal heart rate recordings [ J ]. B JOG : An International Journal of Ob- stetrics & Gynaecology, 2009, 116(2): 334-338.
  • 4Abdulhay E W, Oweis R J, Alhaddad A M, et al. Review article:non-Invasive fetal heart rate monitoring techniques [ J ]. Biomedical Science and Engineering,2014,2 ( 3 ) : 53-67.
  • 5Gruber P, Meyer-Base A, Foo S, et al. ICA, kernel meth- ods and nonnegativity: New paradegms for dynamical com- ponent analysis of fMRI data[ J]. Engineering Applications of Artificial Intelligence, 2009, 22 (4) : 497-504.
  • 6Wenting S, Bin F, Pu W, et al. FECG extraction based on BSS of sparse signal [ C ] //Bioinformatics and Biomedical Engineering, ICBBE 2008. The 2nd International Confer- ence on. Shanghai:IEEE, 2008 : 1457-1460.
  • 7Lee T W, Lewicki M S, Girolami M, et al. Blind source separation of more sources than mixtures using overeomplete representations [ J ]. Signal Processing Letters, IEEE, 1999, 6(4): 87-90.
  • 8Li R, Chen B F. FECG extraction algorithm based on BSS using temporal structure and DWT[ J]. Applied Mechanics and Materials, 2014, 571 : 209-212.
  • 9Liu C, Li P, Di Maria C, et al. A multi-step method with signal quality assessment and fine-tuning procedure to lo- cate maternal and fetal QRS complexes from abdominal ECG recordings [ J]. Physiological Measurement, 2014, 35(8) : 1665.
  • 10蔡坤,王雷,谢胜利,等.胎儿心电检测的时域稀疏性线性混叠盲分离模型的判别方法:中国201110109980.5[P].2011-10-10.

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生物医学工程学杂志
2013年 第6期

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