Abstract
Biomedical signals are in general non-linear and non-stationary. Empirical Mode Decomposition in conjunction with Hilbert-Huang Transform provides a fully adaptive and data-driven technique to extract Intrinsic Mode Functions (IMFs). The latter represent a complete set of orthogonal basis functions to represent non-linear and non-stationary time series. Large scale biomedical time series necessitate an on-line analysis which is presented in this contribution. It shortly reviews the technique of EMD and related algorithms, discusses the newly proposed slidingEMD algorithm and presents some applications to biomedical time series from neuromonitoring.
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References
Flandrin, P., Gonçalvès, P., Rilling, G.: Detrending and denoising with empirical mode decmposition. In: EUSIPCO 2004, pp. 1581–1584 (2004)
Huang, N.E., Shen, Z., Long, S.R., Wu, M.L., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., Liu, H.H.: The empirical mode decomposition and Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc. Roy. Soc. London A 454, 903–995 (1998)
Quiroga, R.Q., Arnhold, J., Grassberger, P.: Learning driver-response relationships from synchronization patterns. Phys. Rev. EÂ 61, 5142 (2000)
Quiroga, R.Q., Kraskov, A., Kreuz, T., Grassberger, P.: Performance of different synchronization measures in real data: A case study on electroencephalographic signals. Phys. Rev. EÂ 65, 041903 (2002)
Rilling, G., Flandrin, P., Goncalès, P.: On empirical mode decomposition and its algorithms. In: Proc. 6th IEEE-EURASIP Workshop on Nonlinear Signal and Image Processing (2003)
Wu, M.-C., Struzik, Z.R., Watanabe, E., Yamamoto, Y., Hu, C.-K.: Temporal evolution for the phase histogram of ECG during human ventricular fibrillation. In: AIP Conf. Proc., vol. 922, pp. 573–576 (2007)
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Zeiler, A., Faltermeier, R., Tomé, A.M., Puntonet, C., Brawanski, A., Lang, E.W. (2011). Sliding Empirical Mode Decomposition for On-line Analysis of Biomedical Time Series. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21501-8_37
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DOI: https://doi.org/10.1007/978-3-642-21501-8_37
Publisher Name: Springer, Berlin, Heidelberg
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