Robust Signal Generation and Analysis of Rat Embryonic Heart Rate in Vitro Using Laplacian Eigenmaps and Empirical Mode Decomposition

  • Muhammad Khalid Khan Niazi
  • Muhammad Talal Ibrahim
  • Mats F. Nilsson
  • Anna-Carin Sköld
  • Ling Guan
  • Ingela Nyström
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6855)


To develop an accurate and suitable method for measuring the embryonic heart rate in vitro, a system combining Laplacian eigenmaps and empirical mode decomposition has been proposed. The proposed method assess the heart activity in two steps; signal generation and heart signal analysis. Signal generation is achieved by Laplacian eigenmaps (LEM) in conjunction with correlation co-efficient, while the signal analysis of the heart motion has been performed by the modified empirical mode decomposition (EMD). LEM helps to find the template for the atrium and the ventricle respectively, whereas EMD helps to find the non-linear trend term without defining any regression model. The proposed method also removes the motion artifacts produced due to the the non-rigid deformation in the shape of the embryo, the noise induced during the data acquisition, and the higher order harmonics. To check the authenticity of the proposed method, 151 videos have been investigated. Experimental results demonstrate the superiority of the proposed method in comparison to three recent methods.


Empirical Mode Decomposition Heart Activity High Order Harmonic Heart Motion Heart Signal 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Webster, W.S., et al.: The effect of hypoxia in development. Birth Defects Res. C. Embryo Today 81, 215–228 (2007)CrossRefGoogle Scholar
  2. 2.
    Doubilet, P.M., et al.: Outcome of first-trimester pregnancies with slow embryonic heart rate at 6-7 weeks gestation and normal heart rate by 8 weeks at US. Radiology 236, 636–643 (2005)CrossRefGoogle Scholar
  3. 3.
    Abela, D., et al.: The effects of drugs with ion channel-blocking activity on the early embryonic rat heart. Birth Defects Res., Part B 89, 429–440 (2010)CrossRefGoogle Scholar
  4. 4.
    Sköld, A.C., et al.: Teratogenicity of the IKr-Blocker Cisapride: Relation to Embryonic Cardiac Arrhythmia. Reprod. Toxicol. 16(4), 333–342 (2002)CrossRefGoogle Scholar
  5. 5.
    Robkin, M.A., et al.: A new in vitro culture technique for rat embryos. Teratology 5, 367–376 (1972)CrossRefGoogle Scholar
  6. 6.
    Khan, M., et al.: Fully automatic heart beat rate determination in digital video recordings of rat embryos. In: Perner, P., Salvetti, O. (eds.) MDA 2008. LNCS (LNAI), vol. 5108, pp. 27–37. Springer, Heidelberg (2008)Google Scholar
  7. 7.
    Fink, M., et al.: A new method for detection and quantification of heartbeat parameters in drosophila, zebrafish, and embryonic mouse hearts. BioTechniques 46, 101–113 (2009)CrossRefGoogle Scholar
  8. 8.
    Chan, P.K., et al.: Noninvasive technique for measurement of heartbeat regularity in zebrafish (Danio rerio) embryos. BMC Biotechnol 9, 1–10 (2009)CrossRefGoogle Scholar
  9. 9.
    Zhu, J.T., et al.: Fast differential interference contrast imaging combined with autocorrelation treatments to measure the heart rate of embryonic fish. J. Biomed. Opt. 13(2) (2008)Google Scholar
  10. 10.
    Belkin, M., et al.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15, 1373–1396 (2003)CrossRefzbMATHGoogle Scholar
  11. 11.
    Oberlin, T., et al.: An Alternative Formulation for the Empirical Mode Decomposition. Tech. Rep. hal-00553107, HAL (January 2011)Google Scholar
  12. 12.
    Huang, N.E., et al.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In: Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, vol. 454, pp. 903–995 (March 1998)Google Scholar
  13. 13.
    Hadji, S.E., et al.: Analysis of Intrinsic Mode Functions: A PDE Approach. IEEE Signal Processing Letters 17, 398–401 (2010)CrossRefGoogle Scholar
  14. 14.
    Hong, H., et al.: Centroid-based sifting for empirical mode decomposition. Journal of Zhejiang University - Science C 12, 88–95 (2011)CrossRefGoogle Scholar
  15. 15.
    Hong, H., et al.: Local integral mean-based sifting for empirical mode decomposition. IEEE Signal Processing Letters 16, 841–844 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Muhammad Khalid Khan Niazi
    • 1
  • Muhammad Talal Ibrahim
    • 2
  • Mats F. Nilsson
    • 3
  • Anna-Carin Sköld
    • 4
  • Ling Guan
    • 2
  • Ingela Nyström
    • 1
  1. 1.Centre for Image AnalysisUppsala UniversitySweden
  2. 2.Ryerson Multimedia Research LabRyerson UniversityTorontoCanada
  3. 3.Department of Pharmaceuticals Biosciences, Drug Safety and ToxicologyUppsala UniversitySweden
  4. 4.AstraZeneca R&D SödertäljeSafety AssessmentSweden

Personalised recommendations