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Shishir Dash & Abhishek Dasgupta: Bidimensional Empirical mode decomposition

Empirical Mode Decomposition is a new technique used to decompose a given signal into other signals which are orthogonal to each other. This technique, first proposed by Huang, is unique in the sense that it is parameter-less and requires no basis functions to decompose any given signals. We are applying this method on two different types of data.

On the first hand we have cardiovascular data pertaining to the R-R intervals of the heartbeat. Empirical Mode Decomposition is used to analyse the data for its nonstationarity – a parameter which cannot be determined reliably by other methods. Also EMD can be use to find the intrinsic functions of the cardiovascular data which can then be utilised to find intrinsic time scales of th data.

On the other hand we also apply the concept of Empirical mode Decomposition on 2D data. The 2D data in question are grayscale images of the trabecular bone structure of tibia. Empirical mode decomposition is used to find the complexities of the bone structure. Various modes of this bone structure is also symbol encoded for further analysis.

References

1. The Empirical Mode Decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.

Authors: Norden E. Huang, Zeng Shen, Steven R. Long, Manli C. Wu, Hsing H. Shih, Quanan Zheng, Nai-Chuyan Yen, Chi Chao Tung and Nai-Chyuan Yen

The Royal Society

Proc. R. Soc. Lond. A (1998) 454, 903-995

2. Image Analysis by Bidimensional Empirical Mode Decomposition

Authors: J.C. Nunes, Y. Bouaoune, E. Delechelle, O. Niang, Ph. Bunel

Image and Vision Computing - 2003, 21, 1019-1026

3. Quantification of cancellous bone structure using symbolic dynamics and measures of complexity

Authors: Peter I. Saparin, Wolfgang Gowin, Jurgen Kurths, Dieter Felsenberg

Physical Review E, Volume 58, Number 5 - November 1998

4. Processing of 2D images from different skeletal locations and preliminary results of complesity estimations

Presentation given by Peter Saparin

December 18, 2001

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