A fast algorithm for automated independent process separation from single channel biomedical signal recordings: Fast IPA
A fast algorithm for automated independent process separation from single channel biomedical signal recordings: Fast IPA
Independent component analysis (ICA) has found many uses in source separation in biomedical signals. We highlight a methodology and put forward an algorithm which allows single channel ICA to be performed on single channel biomedical signal recordings. The algorithm uses a fast, deflationary approach to efficiently extract independent processes underlying the single channel recordings. We show that for processes which are reasonably spectrally disjoint the algorithm can separate out individual sources. We show examples of this using brain signal recordings and abdominal foetal recordings.
blind source separation, independent component analysis (ICA), single channel ICA, automated source extraction, independent process analysis
9780863419348
1-4
Institute of Engineering and Technology, IET
James, C.J.
b3733b1f-a6a1-4c9b-b75c-6191d4142e52
Davies, M.E.
2f97d5ab-efda-4d6f-936d-00ae95d19e65
July 2008
James, C.J.
b3733b1f-a6a1-4c9b-b75c-6191d4142e52
Davies, M.E.
2f97d5ab-efda-4d6f-936d-00ae95d19e65
James, C.J. and Davies, M.E.
(2008)
A fast algorithm for automated independent process separation from single channel biomedical signal recordings: Fast IPA.
In 4th IET International Conference on Advances in Medical, Signal and Information Processing, 2008. MEDSIP 2008.
Institute of Engineering and Technology, IET.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Independent component analysis (ICA) has found many uses in source separation in biomedical signals. We highlight a methodology and put forward an algorithm which allows single channel ICA to be performed on single channel biomedical signal recordings. The algorithm uses a fast, deflationary approach to efficiently extract independent processes underlying the single channel recordings. We show that for processes which are reasonably spectrally disjoint the algorithm can separate out individual sources. We show examples of this using brain signal recordings and abdominal foetal recordings.
This record has no associated files available for download.
More information
Published date: July 2008
Additional Information:
1 CD-ROM : col. ; 4 3/4 in.
Venue - Dates:
Proceedings of MEDSIP 2008 4th IET International Conference on Advances in Medical Signal and Information Processing, Sta Margherita Ligure, Italy, 14-16 July, Sta Margherita Ligure, Italy, 2008-07-13 - 2008-07-15
Keywords:
blind source separation, independent component analysis (ICA), single channel ICA, automated source extraction, independent process analysis
Identifiers
Local EPrints ID: 65184
URI: http://eprints.soton.ac.uk/id/eprint/65184
ISBN: 9780863419348
PURE UUID: d3f77475-b39e-4561-a687-0bbc08d3faa9
Catalogue record
Date deposited: 04 Mar 2009
Last modified: 08 Jan 2022 07:09
Export record
Contributors
Author:
C.J. James
Author:
M.E. Davies
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics