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Blind source separation of cardiac murmurs from heart recordings

Pietila, A ; El-Segaier, Milad LU ; Vigario, R and Pesonen, Erkki LU (2006) 6th International Conference, ICA 2006 3889. p.470-477
Abstract
A significant percentage of young children present cardiac murmurs. However, only one percent of them are caused by a congenital heart defect; others are physiological. Auscultation of the heart is still the primary diagnostic tool for judging the type of cardiac murmur. An automated system for an initial recording and analysis of the cardiac sounds could enable the primary care physicians to make the initial diagnosis and thus decrease the workload of the specialised health care system. The first step in any automated murmur classifier is the identification of different components of cardiac cycle and separation of the murmurs. Here we propose a new methodological framework to address this issue from a machine learning perspective,... (More)
A significant percentage of young children present cardiac murmurs. However, only one percent of them are caused by a congenital heart defect; others are physiological. Auscultation of the heart is still the primary diagnostic tool for judging the type of cardiac murmur. An automated system for an initial recording and analysis of the cardiac sounds could enable the primary care physicians to make the initial diagnosis and thus decrease the workload of the specialised health care system. The first step in any automated murmur classifier is the identification of different components of cardiac cycle and separation of the murmurs. Here we propose a new methodological framework to address this issue from a machine learning perspective, combining Independent Component Analysis and Denoising Source Separation. We show that such a method is rather efficient in the separation of cardiac murmurs. The framework is equally capable of separating heart sounds S1 and S2 and artifacts such as voices recorded during the measurements. (Less)
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author
; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Independent Component Analysis and Blind Signal Separation. Proceedings (Lecture Notes in Computer Science)
volume
3889
pages
470 - 477
publisher
Springer
conference name
6th International Conference, ICA 2006
conference location
Charleston, SC, United States
conference dates
2006-03-05 - 2006-03-08
external identifiers
  • wos:000236486300059
  • scopus:33745683243
ISSN
1611-3349
0302-9743
ISBN
978-3-540-32630-4
DOI
10.1007/11679363_59
language
English
LU publication?
yes
id
0cd99c9a-3991-4ee0-b8ca-f66a5522cabb (old id 414063)
date added to LUP
2016-04-01 11:53:18
date last changed
2021-02-17 04:43:03
@inproceedings{0cd99c9a-3991-4ee0-b8ca-f66a5522cabb,
  abstract     = {A significant percentage of young children present cardiac murmurs. However, only one percent of them are caused by a congenital heart defect; others are physiological. Auscultation of the heart is still the primary diagnostic tool for judging the type of cardiac murmur. An automated system for an initial recording and analysis of the cardiac sounds could enable the primary care physicians to make the initial diagnosis and thus decrease the workload of the specialised health care system. The first step in any automated murmur classifier is the identification of different components of cardiac cycle and separation of the murmurs. Here we propose a new methodological framework to address this issue from a machine learning perspective, combining Independent Component Analysis and Denoising Source Separation. We show that such a method is rather efficient in the separation of cardiac murmurs. The framework is equally capable of separating heart sounds S1 and S2 and artifacts such as voices recorded during the measurements.},
  author       = {Pietila, A and El-Segaier, Milad and Vigario, R and Pesonen, Erkki},
  booktitle    = {Independent Component Analysis and Blind Signal Separation. Proceedings (Lecture Notes in Computer Science)},
  isbn         = {978-3-540-32630-4},
  issn         = {1611-3349},
  language     = {eng},
  pages        = {470--477},
  publisher    = {Springer},
  title        = {Blind source separation of cardiac murmurs from heart recordings},
  url          = {http://dx.doi.org/10.1007/11679363_59},
  doi          = {10.1007/11679363_59},
  volume       = {3889},
  year         = {2006},
}