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Detection of systolic ejection click using time growing neural network.

Gharehbaghi, Arash; Dutoit, Thierry; Ask, Per and Sörnmo, Leif LU (2014) In Medical Engineering & Physics 36(4). p.477-483
Abstract
In this paper, we present a novel neural network for classification of short-duration heart sounds: the time growing neural network (TGNN). The input to the network is the spectral power in adjacent frequency bands as computed in time windows of growing length. Children with heart systolic ejection click (SEC) and normal children are the two groups subjected to analysis. The performance of the TGNN is compared to that of a time delay neural network (TDNN) and a multi-layer perceptron (MLP), using training and test datasets of similar sizes with a total of 614 normal and abnormal cardiac cycles. From the test dataset, the classification rate/sensitivity is found to be 97.0%/98.1% for the TGNN, 85.1%/76.4% for the TDNN, and 92.7%/85.7% for... (More)
In this paper, we present a novel neural network for classification of short-duration heart sounds: the time growing neural network (TGNN). The input to the network is the spectral power in adjacent frequency bands as computed in time windows of growing length. Children with heart systolic ejection click (SEC) and normal children are the two groups subjected to analysis. The performance of the TGNN is compared to that of a time delay neural network (TDNN) and a multi-layer perceptron (MLP), using training and test datasets of similar sizes with a total of 614 normal and abnormal cardiac cycles. From the test dataset, the classification rate/sensitivity is found to be 97.0%/98.1% for the TGNN, 85.1%/76.4% for the TDNN, and 92.7%/85.7% for the MLP. The results show that the TGNN performs better than do TDNN and MLP when frequency band power is used as classifier input. The performance of TGNN is also found to exhibit better immunity to noise. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Medical Engineering & Physics
volume
36
issue
4
pages
477 - 483
publisher
Elsevier
external identifiers
  • pmid:24613501
  • wos:000334976800008
  • scopus:84897107561
ISSN
1873-4030
DOI
10.1016/j.medengphy.2014.02.011
language
English
LU publication?
yes
id
a1f92f43-46ac-444d-8d61-4c26e16b708a (old id 4383531)
date added to LUP
2014-04-25 13:43:40
date last changed
2017-01-01 03:00:13
@article{a1f92f43-46ac-444d-8d61-4c26e16b708a,
  abstract     = {In this paper, we present a novel neural network for classification of short-duration heart sounds: the time growing neural network (TGNN). The input to the network is the spectral power in adjacent frequency bands as computed in time windows of growing length. Children with heart systolic ejection click (SEC) and normal children are the two groups subjected to analysis. The performance of the TGNN is compared to that of a time delay neural network (TDNN) and a multi-layer perceptron (MLP), using training and test datasets of similar sizes with a total of 614 normal and abnormal cardiac cycles. From the test dataset, the classification rate/sensitivity is found to be 97.0%/98.1% for the TGNN, 85.1%/76.4% for the TDNN, and 92.7%/85.7% for the MLP. The results show that the TGNN performs better than do TDNN and MLP when frequency band power is used as classifier input. The performance of TGNN is also found to exhibit better immunity to noise.},
  author       = {Gharehbaghi, Arash and Dutoit, Thierry and Ask, Per and Sörnmo, Leif},
  issn         = {1873-4030},
  language     = {eng},
  number       = {4},
  pages        = {477--483},
  publisher    = {Elsevier},
  series       = {Medical Engineering & Physics},
  title        = {Detection of systolic ejection click using time growing neural network.},
  url          = {http://dx.doi.org/10.1016/j.medengphy.2014.02.011},
  volume       = {36},
  year         = {2014},
}