A new automatic method of parkinson disease identification using complex-valued neural network
A new automatic method of Parkinson detection and classification using Complex Valued Neural Network (CVNN) is proposed in this paper. The proposed methodology used one of recently introduced dysphonia measure as part of its input data. The selected measures are those that are robust to many uncontro...
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2017
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my.iium.irep.412222018-01-12T10:09:01Z http://irep.iium.edu.my/41222/ A new automatic method of parkinson disease identification using complex-valued neural network Olanrewaju, Rashidah Funke Zaharii, Nur Syarafina Aibinu, Abiodun Musa Q Science (General) R Medicine (General) A new automatic method of Parkinson detection and classification using Complex Valued Neural Network (CVNN) is proposed in this paper. The proposed methodology used one of recently introduced dysphonia measure as part of its input data. The selected measures are those that are robust to many uncontrollable variations in individual and environments. The three selected dysphonia measures are converted from time domain to frequency domain by application of Discrete Fourier Transform (DFT) on the data. The frequency domain converted measures are fed to CVNN and the output of CVNN serves as input to the parkinson disease classifier for classification purpose. Result obtained by application of this technique on parkinson data resulted in classification performance of 96% accuracy JOMB Editorial 2017-06 Article REM application/pdf en http://irep.iium.edu.my/41222/1/A%20New%20Automatic%20Method%20of%20Parkinson%20Disease.pdf Olanrewaju, Rashidah Funke and Zaharii, Nur Syarafina and Aibinu, Abiodun Musa (2017) A new automatic method of parkinson disease identification using complex-valued neural network. Journal of Medical and Bioengineering, 6 (1). pp. 25-28. ISSN 2301-3796 http://www.jomb.org/ 10.18178/jomb.6.1.25-28 |
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Q Science (General) R Medicine (General) Olanrewaju, Rashidah Funke Zaharii, Nur Syarafina Aibinu, Abiodun Musa A new automatic method of parkinson disease identification using complex-valued neural network |
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A new automatic method of Parkinson detection and classification using Complex Valued Neural Network (CVNN) is proposed in this paper. The proposed methodology used one of recently introduced dysphonia measure as part of its input data. The selected measures are those that are robust to many uncontrollable variations in individual and environments. The three selected dysphonia measures are converted from time domain to frequency domain by application of Discrete Fourier Transform (DFT) on the data. The frequency domain converted measures are fed to CVNN and the output of CVNN serves as input to the parkinson disease classifier for classification purpose. Result obtained by application of this technique on parkinson data resulted in classification performance of 96% accuracy |
format |
Article |
author |
Olanrewaju, Rashidah Funke Zaharii, Nur Syarafina Aibinu, Abiodun Musa |
author_facet |
Olanrewaju, Rashidah Funke Zaharii, Nur Syarafina Aibinu, Abiodun Musa |
author_sort |
Olanrewaju, Rashidah Funke |
title |
A new automatic method of parkinson disease identification using complex-valued neural network |
title_short |
A new automatic method of parkinson disease identification using complex-valued neural network |
title_full |
A new automatic method of parkinson disease identification using complex-valued neural network |
title_fullStr |
A new automatic method of parkinson disease identification using complex-valued neural network |
title_full_unstemmed |
A new automatic method of parkinson disease identification using complex-valued neural network |
title_sort |
new automatic method of parkinson disease identification using complex-valued neural network |
publisher |
JOMB Editorial |
publishDate |
2017 |
url |
http://irep.iium.edu.my/41222/1/A%20New%20Automatic%20Method%20of%20Parkinson%20Disease.pdf http://irep.iium.edu.my/41222/ http://www.jomb.org/ |
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13.188404 |