A new automatic method of parkinson disease identiﬁcation using complex-valued neural network - Olanrewaju, Rashidah Funke; Zaharii, Nur Syarafina; Aibinu, Abiodun Musa (JOMB Editorial, 2017)
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