ECG-Driven Extraction of Respiration Rate Using Ensemble Empirical Mode Decomposition and Canonical Correlation Analysis

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)

Abstract

Respiratory signal and electrocardiogram are correlated to each other. In this paper, respiration rate has been extracted from ECG. We purpose a novel combination of Canonical Correlation Analysis (CCA) and Ensemble Empirical Mode Decomposition (EEMD) in order to remove the artifacts, and we have estimated the respiratory rate from the denoised ECG by creating the envelope of the denoised signal. The canonical correlation corresponding to the artifacts was removed on the basis of correlation coefficient of denoised signal and ground truth signal. The MIT-Polysomonographic and Apnea-ECG databases of physionet bank were used to acquire the ECG signals. Real-time Baseline wander noise from MIT-NSTDB was added to each record, and the respiratory rate determined was compared with the corresponding respiratory signals. The average signal-to-noise ratio improvement in case of denoising using EEMD-CCA is 20.8989 db. The average BPM error in respiration rate derived from ECG denoised from EEMD is ±2.5 BPM.

Keywords

Canonical correlation analysis (CCA) Ensemble empirical mode decomposition (EEMD) Intrinsic mode function (IMF) Canonical component (CC) 

References

  1. 1.
    Pallas-Areny, R., Colominas-Balague, J., Rosell, F.J.: The effect of respiration-induced heart movements on the ECG. Proc. IEEE Trans. Biomed. Eng. 36(6), 585–590 (1989)Google Scholar
  2. 2.
    Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A, Math. Phys Eng. Sci. 454(1971), 903–995 (1998)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Widjaja, D., Varon, C., Dorado, A.C., Suykens, J.A.K., Van Huffel, S.: Application of Kernal principal component analysis for single-lead-ECG-derived respiration. In: Proceedings of Biomedical Transactions, vol. 59, no. 4, pp. 1169–1176 (2012)Google Scholar
  4. 4.
    Madhav, K.V., Ram, M.R., Krishna, E.H., Reddy, K.A.: Estimation of respiration rate from ECG, BP and PPG signals using empirical mode decomposition. In: Proceedings of IEEE International Conference I2MTC-2011, pp. 1–4 (2011)Google Scholar
  5. 5.
    Wu, Z., Huang, N.E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method. Adv. Adapt. Data Anal. 1(1), 1–41 (2009)Google Scholar
  6. 6.
    Singh, G., Kaur, G., Kumar, V.: ECG Denoising using adaptive selection of IMFs through EMD and EEMD. In: International Conference on Data Science & Engineering (ICDSE), pp. 228–231 (2014)Google Scholar
  7. 7.
    Zhang, J., Yan, R., Gao, R.X., Feng, Z.: Performance enhancement of ensemble empirical mode decomposition. In: Proceedings of Mechanical System and Signal Processing, vol. 24, pp. 2104–2123 (2010)Google Scholar
  8. 8.
    Borga, M.: Canonical Correlation a Tutorial, Jan 2001Google Scholar
  9. 9.
    Sweeney, K.T., McLoone, S.F., Ward, T.E.: The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technnique. IEEE Trans. Biomed. Eng. 60(1), 97–105 (2013)Google Scholar
  10. 10.
    Hassan, M., Boudaoud, S., Terrien, J., Karlsson, B., Marque, C.: Combination of Canonical Correlation Analysis and Empirical Mode Decomposition Applied to Denoising the Labor Electrohysterogram. In: Proceedings of IEEE Transactions on Biomedical Engineering, vol. 58, no. 9, Sept 2011Google Scholar
  11. 11.
    Torres, M.E.C., Schlotthauer, G., Flandrin, P.: A complete ensemble mode decomposition with adaptive noise. In: Proceedings of IEEE International Conference ICASSP-2011, pp. 4144–4147, 2011Google Scholar
  12. 12.

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringLovely Professional UniversityPhagwaraIndia

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