A new automatic method of parkinson disease identification using complex-valued neural network

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

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Abstract

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

Item Type:	Article (Journal)
Additional Information:	6796/41222
Uncontrolled Keywords:	Terms—complex backpropagation algorithm, Complex-Valued Data (CVD), Complex Valued Neural Network (CVNN), Fast Fourier Transform (FFT), parkinson disease (PD)
Subjects:	Q Science > Q Science (General) R Medicine > R Medicine (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr. Rashidah Funke Olanrewaju
Date Deposited:	31 Jul 2017 08:58
Last Modified:	12 Jan 2018 18:09
URI:	http://irep.iium.edu.my/id/eprint/41222

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