NN-Based FECG extraction from the composite abdominal ECG
FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during pregnancy and labour. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies is...
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iium-362262014-04-16T01:56:00Z http://irep.iium.edu.my/36226/ NN-Based FECG extraction from the composite abdominal ECG Hasan, Muhammad Asraful Ibrahimy, Muhammad Ibn Reaz, Mamun Bin Ibne T Technology (General) FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appropriate and timely decisions during pregnancy and labour. The extraction and detection of the FECG signal from composite abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of thi s paper is to illustrate the developed algorithms on FECG signal extraction from the abdominal ECG signal using Neural Network approach is to provide efficient and effective ways of separating and understanding the FECG signal and its nature. The FECG signal was isolated from the abdominal signal by neural network approach with different learning constant value and momentum as well so that acceptable signal can be considered. According to the output it can be said that the algorithm is working satisfactory on high learning rate and low momentum value. The method appears to be exceedingly robust, correctly isolate the FECG signal from abdominal ECG. 2008 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/36226/1/NN-BASED_FECG_EXTRACTION_FROM_THE_COMPOSITE_ABDOMINAL_ECG.pdf Hasan, Muhammad Asraful and Ibrahimy, Muhammad Ibn and Reaz, Mamun Bin Ibne (2008) NN-Based FECG extraction from the composite abdominal ECG. In: 38th International Conference on Computers and Industrial Engineering, 31 Oct.-2 Nov., 2008, Beijing, China. |
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Online Access |
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English |
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T Technology (General) |
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T Technology (General) Hasan, Muhammad Asraful Ibrahimy, Muhammad Ibn Reaz, Mamun Bin Ibne NN-Based FECG extraction from the composite abdominal ECG |
description |
FECG (Fetal ECG) signal contains potentially
precise information that could assist clinicians in
making more appropriate and timely decisions
during pregnancy and labour. The extraction and
detection of the FECG signal from composite
abdominal signals with powerful and advance
methodologies is becoming a very important
requirement in fetal monitoring. The purpose of thi
s
paper is to illustrate the developed algorithms on
FECG signal extraction from the abdominal ECG
signal using Neural Network approach is to provide
efficient and effective ways of separating and
understanding the FECG signal and its nature. The
FECG signal was isolated from the abdominal signal
by neural network approach with different learning
constant value and momentum as well so that
acceptable signal can be considered. According to
the output it can be said that the algorithm is
working satisfactory on high learning rate and low
momentum value. The method appears to be
exceedingly robust, correctly isolate the FECG
signal from abdominal ECG. |
format |
Conference or Workshop Item |
author |
Hasan, Muhammad Asraful Ibrahimy, Muhammad Ibn Reaz, Mamun Bin Ibne |
author_facet |
Hasan, Muhammad Asraful Ibrahimy, Muhammad Ibn Reaz, Mamun Bin Ibne |
author_sort |
Hasan, Muhammad Asraful |
title |
NN-Based FECG extraction from the composite abdominal ECG |
title_short |
NN-Based FECG extraction from the composite abdominal ECG |
title_full |
NN-Based FECG extraction from the composite abdominal ECG |
title_fullStr |
NN-Based FECG extraction from the composite abdominal ECG |
title_full_unstemmed |
NN-Based FECG extraction from the composite abdominal ECG |
title_sort |
nn-based fecg extraction from the composite abdominal ecg |
publishDate |
2008 |
url |
http://irep.iium.edu.my/36226/ http://irep.iium.edu.my/36226/1/NN-BASED_FECG_EXTRACTION_FROM_THE_COMPOSITE_ABDOMINAL_ECG.pdf |
first_indexed |
2023-09-18T20:51:51Z |
last_indexed |
2023-09-18T20:51:51Z |
_version_ |
1777410046493196288 |