Abstract

Effects of EEG Signal to Parkinson Disease through Deep Recurrent Neural Network

This paper introduced a novel computer-aided diagnosis method to detect the Parkinson Disease (PD). A novel Pooling-based Deep Recurrent Neural Network (PDRNN) is used in this proposed algorithm as an efficient deep learning method. Parkinson disease gradually degrades the functionality of the brain. Because of its relevance to the abnormality of the brain, EEG (denoting the electroencephalogram) signal is used for early detection of this disease. The electroencephalogram signals of 20 Parkinson and 20 healthy cases are studied in this paper. Also, a PDRNN learning method is applied on the used dataset for tackling the demand for the traditional feature presentation step. The proposed method of this paper could obtain proper precision, sensitivity and specificity (88.31, 84.84 and 91.81 percent, respectively). In addition, our derived classification method has potential to be employed for high populations prior to be installed for clinical applications.

Author(s):

Saeid Gholami Farkoush*

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