A novel signal diagnosis technique using pseudo complex-valued autoregressive technique

In this paper, a new method of biomedical signal classification using complex- valued pseudo autoregressive (CAR) modeling approach has been proposed. The CAR coefficients were computed from the synaptic weights and coefficients of a split weight and activation function of a feedforward multilayer...

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Main Authors: Aibinu, Abiodun Musa, Salami, Momoh Jimoh Emiyoka, Shafie, Amir Akramin
Format: Article
Language:English
Published: Elsevier 2011
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Online Access:http://irep.iium.edu.my/1476/1/ESWA.pdf
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spelling my.iium.irep.14762011-10-03T06:59:55Z http://irep.iium.edu.my/1476/ A novel signal diagnosis technique using pseudo complex-valued autoregressive technique Aibinu, Abiodun Musa Salami, Momoh Jimoh Emiyoka Shafie, Amir Akramin TA165 Engineering instruments, meters, etc. Industrial instrumentation In this paper, a new method of biomedical signal classification using complex- valued pseudo autoregressive (CAR) modeling approach has been proposed. The CAR coefficients were computed from the synaptic weights and coefficients of a split weight and activation function of a feedforward multilayer complex valued neural network. The performance of the proposed technique has been evaluated using PIMA Indian diabetes dataset with different complex-valued data normalization techniques and four different values of learning rate. An accuracy value of 81.28% has been obtained using this proposed technique. Elsevier 2011 Article REM application/pdf en http://irep.iium.edu.my/1476/1/ESWA.pdf Aibinu, Abiodun Musa and Salami, Momoh Jimoh Emiyoka and Shafie, Amir Akramin (2011) A novel signal diagnosis technique using pseudo complex-valued autoregressive technique. Expert Systems with Application, 38 (8). pp. 9063-9069. ISSN 0957-4174 http://www.elsevier.com/wps/find/journaldescription.cws_home/939/description#description 10.1016/j.eswa.2010.11.005
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
topic TA165 Engineering instruments, meters, etc. Industrial instrumentation
spellingShingle TA165 Engineering instruments, meters, etc. Industrial instrumentation
Aibinu, Abiodun Musa
Salami, Momoh Jimoh Emiyoka
Shafie, Amir Akramin
A novel signal diagnosis technique using pseudo complex-valued autoregressive technique
description In this paper, a new method of biomedical signal classification using complex- valued pseudo autoregressive (CAR) modeling approach has been proposed. The CAR coefficients were computed from the synaptic weights and coefficients of a split weight and activation function of a feedforward multilayer complex valued neural network. The performance of the proposed technique has been evaluated using PIMA Indian diabetes dataset with different complex-valued data normalization techniques and four different values of learning rate. An accuracy value of 81.28% has been obtained using this proposed technique.
format Article
author Aibinu, Abiodun Musa
Salami, Momoh Jimoh Emiyoka
Shafie, Amir Akramin
author_facet Aibinu, Abiodun Musa
Salami, Momoh Jimoh Emiyoka
Shafie, Amir Akramin
author_sort Aibinu, Abiodun Musa
title A novel signal diagnosis technique using pseudo complex-valued autoregressive technique
title_short A novel signal diagnosis technique using pseudo complex-valued autoregressive technique
title_full A novel signal diagnosis technique using pseudo complex-valued autoregressive technique
title_fullStr A novel signal diagnosis technique using pseudo complex-valued autoregressive technique
title_full_unstemmed A novel signal diagnosis technique using pseudo complex-valued autoregressive technique
title_sort novel signal diagnosis technique using pseudo complex-valued autoregressive technique
publisher Elsevier
publishDate 2011
url http://irep.iium.edu.my/1476/1/ESWA.pdf
http://irep.iium.edu.my/1476/
http://www.elsevier.com/wps/find/journaldescription.cws_home/939/description#description
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score 13.160551