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Computational and Mathematical Methods in Medicine - Volume 8 2007, Issue 4, Pages 263-285

Original Article Department of Electrical Engineering, Indian Institute of Technology, Roorkee 247667, India

Received 12 March 2007; Revised 3 September 2007; Accepted 24 October 2007

Copyright © 2007 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Principal component analysis PCA is used to reduce dimensionality of electrocardiogram ECG data prior to performing independent component analysis ICA. A newly developed PCA variance estimator by the author has been applied for detecting true, actual and false peaks of ECG data files. In this paper, it is felt that the ability of ICA is also checked for parameterization of ECG signals, which is necessary at times. Independent components ICs of properly parameterized ECG signals are more readily interpretable than the measurements themselves, or their ICs. The original ECG recordings and the samples are corrected by statistical measures to estimate the noise statistics of ECG signals and find the reconstruction errors. The capability of ICA is justified by finding the true, false and actual peaks of around 25–50, CSE common standards for electrocardiography database ECG files. In the present work, joint approximation for diagonalization of the eigen matrices Jade algorithm is applied to 3-channel ECG. ICA processing of different cases is dealt with and the R-peak magnitudes of the ECG waveforms before and after applying ICA are found and marked. ICA results obtained indicate that in most of the cases, the percentage error in reconstruction is very small. The developed PCA variance estimator along with the quadratic spline wavelet gave a sensitivity of 97.47% before applying ICA and 98.07% after ICA processing.

Autor: M. P. S. Chawla



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