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.
Biomedical Engineering Letters – Springer Journals
Published: Apr 1, 2017
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
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
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.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera