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Analysis and Interpretation of Biomedical Signals using Component Extraction Techniques

Published on May 2012 by Hemant P. Kasturiwale
National Conference on Advancement in Electronics & Telecommunication Engineering
Foundation of Computer Science USA
NCAETE - Number 2
May 2012
Authors: Hemant P. Kasturiwale
9481a01d-a234-4166-ab2f-173d73c92d11

Hemant P. Kasturiwale . Analysis and Interpretation of Biomedical Signals using Component Extraction Techniques. National Conference on Advancement in Electronics & Telecommunication Engineering. NCAETE, 2 (May 2012), 1-4.

@article{
author = { Hemant P. Kasturiwale },
title = { Analysis and Interpretation of Biomedical Signals using Component Extraction Techniques },
journal = { National Conference on Advancement in Electronics & Telecommunication Engineering },
issue_date = { May 2012 },
volume = { NCAETE },
number = { 2 },
month = { May },
year = { 2012 },
issn = 0975-8887,
pages = { 1-4 },
numpages = 4,
url = { /proceedings/ncaete/number2/6595-1085/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancement in Electronics & Telecommunication Engineering
%A Hemant P. Kasturiwale
%T Analysis and Interpretation of Biomedical Signals using Component Extraction Techniques
%J National Conference on Advancement in Electronics & Telecommunication Engineering
%@ 0975-8887
%V NCAETE
%N 2
%P 1-4
%D 2012
%I International Journal of Computer Applications
Abstract

Biomedical signals can arise from one or many sources including heart, brains and endocrine systems. Multiple sources poses challenge to researchers which may have contaminated with artifacts and noise. The analysis of these signals is important both for research and for medical diagnosis and treatment. The applications of Independent Component Analysis (ICA) to biomedical signals is a rapidly expanding area of research and many groups are now actively engaged in exploring the potential of blind signal separation and signal deconvolution for revealing new information about the brain and body . The Biomedical time series signal like elctroencephalogram(EEG), electrocardiogram(ECG), etc The morphology of the cardiac signal is very important in most of diagnostics based on the ECG. The immense scope in the field of biomedical-signal processing Independent Component Analysis( ICA ) is gaining momentum due to huge data base requirement for quality testing . The diagnosis of patient is based on visual observation of recorded ECG, EEG, etc. may not be accurate. To achieve better understanding PCA (Principal Component Analysis) and ICA algorithms helps in analyzing ECG signals. This paper describes some algorithms of ICA in brief, such as Fast-ICA, Kernel-ICA, MS –ICA, JADE, EGLD-ICA ,etc. The experimental results presented in the paper show that the SNR proposed here to indentify the various components with higher accuracy in the particular algorithm based on classifying biomedical data.

References
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  2. A. Hyvarinen, J. Karhunen, and E. Oja. Independent Component analysis. Wiley, 2001
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  7. R Batch, Michael Jorden, "Kernel Independent Component Analysis," Journal of Machine Learning Research, Vol. 3 pp-1-48,2002.
  8. JADE- The JADE MATLAB package. 1999.
  9. Hyvarinen, A. and Oja, E. (1997) A fast fixed- point algorithm for independent component analysis. Neural Computation, 9:1483-92.
  10. The MIT-BIH Noise Stress Test Database.
  11. B D. Moor, Database for the Identification of Systems (DaISy)
Index Terms

Computer Science
Information Sciences

Keywords

Cbs(complex Biomedical Signals) Eeg(electroenphalogram) Ecg(electrocardiograph) Pca(principal Component Analysis) ica(independent Component Analysis) Algorithms Snr Signal Processing