Dimensional contraction by principal component analysis as preprocessing for independent component analysis at MCG

Dimensional contraction by principal component analysis as preprocessing for independent... We propose a noise reduction method for magnetocardiograms (MCGs) based on independent component analysis (ICA). ICA is useful to separate the noise and signal components, but ICA-based automatic noise reduction faces two main difficulties: the dimensional contraction process applied after the principal component analysis (PCA) used for preprocessing, and the component selection applied after ICA. The results of noise reduction vary among people, because these two processes typically depend on personal qualitative evaluations of the obtained components. Therefore, automatic quantitative ICA-based noise reduction is highly desirable. We will focus on the first difficulty, by improving the index used in the dimensional contraction process. The index used for component ordering after PCA affects the accuracy of separation obtained with ICA. The contribution ratio is often used as an index. However, its efficacy is highly dependent on the signal-to-noise ratio (SNR) it unsuitable for automation. We propose a kurtosis-based index, whose efficacy does not depend on SNR. We compare the two decision indexes through simulation. First, we evaluate their preservation rate of the MCG information after dimensional contraction. In addition, we evaluate their effect on the accuracy of the ICA-based noise reduction method. The obtained results show that the kurtosis-based index does preserve the MCG signal information through dimensional contraction, and has a more consistent behavior when the number of components increases. The proposed index performs better than the traditional index, especially in low SNRs. As such, it paves the way for the desired noise reduction process automation. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biomedical Engineering Letters Springer Journals

Dimensional contraction by principal component analysis as preprocessing for independent component analysis at MCG

Loading next page...
 
/lp/springer_journal/dimensional-contraction-by-principal-component-analysis-as-r3c0GBgagk
Publisher
Springer Journals
Copyright
Copyright © 2017 by Korean Society of Medical and Biological Engineering and Springer
Subject
Engineering; Biomedical Engineering; Biological and Medical Physics, Biophysics; Biomedicine, general; Medical and Radiation Physics
ISSN
2093-9868
eISSN
2093-985X
D.O.I.
10.1007/s13534-017-0024-5
Publisher site
See Article on Publisher Site

Abstract

We propose a noise reduction method for magnetocardiograms (MCGs) based on independent component analysis (ICA). ICA is useful to separate the noise and signal components, but ICA-based automatic noise reduction faces two main difficulties: the dimensional contraction process applied after the principal component analysis (PCA) used for preprocessing, and the component selection applied after ICA. The results of noise reduction vary among people, because these two processes typically depend on personal qualitative evaluations of the obtained components. Therefore, automatic quantitative ICA-based noise reduction is highly desirable. We will focus on the first difficulty, by improving the index used in the dimensional contraction process. The index used for component ordering after PCA affects the accuracy of separation obtained with ICA. The contribution ratio is often used as an index. However, its efficacy is highly dependent on the signal-to-noise ratio (SNR) it unsuitable for automation. We propose a kurtosis-based index, whose efficacy does not depend on SNR. We compare the two decision indexes through simulation. First, we evaluate their preservation rate of the MCG information after dimensional contraction. In addition, we evaluate their effect on the accuracy of the ICA-based noise reduction method. The obtained results show that the kurtosis-based index does preserve the MCG signal information through dimensional contraction, and has a more consistent behavior when the number of components increases. The proposed index performs better than the traditional index, especially in low SNRs. As such, it paves the way for the desired noise reduction process automation.

Journal

Biomedical Engineering LettersSpringer Journals

Published: Apr 1, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off