Sliding Empirical Mode Decomposition for On-line Analysis of Biomedical Time Series
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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- 1.Flandrin, P., Gonçalvès, P., Rilling, G.: Detrending and denoising with empirical mode decmposition. In: EUSIPCO 2004, pp. 1581–1584 (2004)Google Scholar
- 5.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)Google Scholar
- 6.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)Google Scholar