Detection of systolic ejection click using time growing neural network.
(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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4383531
- author
- Gharehbaghi, Arash ; Dutoit, Thierry ; Ask, Per and Sörnmo, Leif LU
- organization
- publishing date
- 2014
- 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
- pmid:24613501
- 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
- 2016-04-01 09:47:38
- date last changed
- 2022-04-27 07:33:53
@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}},
doi = {{10.1016/j.medengphy.2014.02.011}},
volume = {{36}},
year = {{2014}},
}