Gaborpdnet: Gabor transformation and deep neural network for parkinson’s disease detection using eeg signals
Article
Loh, Hui Wen, Ooi, Chui Ping, Palmer, Elizabeth, Barua, Prabal Datta, Dogan, Sengul, Tuncer, Turker, Baygin, Mehmet and Acharya, U. Rajendra. 2021. "Gaborpdnet: Gabor transformation and deep neural network for parkinson’s disease detection using eeg signals." Electronics. 10 (14). https://doi.org/10.3390/electronics10141740
| Article Title | Gaborpdnet: Gabor transformation and deep neural network for parkinson’s disease detection using eeg signals |
|---|---|
| ERA Journal ID | 210405 |
| Article Category | Article |
| Authors | Loh, Hui Wen, Ooi, Chui Ping, Palmer, Elizabeth, Barua, Prabal Datta, Dogan, Sengul, Tuncer, Turker, Baygin, Mehmet and Acharya, U. Rajendra |
| Journal Title | Electronics |
| Journal Citation | 10 (14) |
| Article Number | 1740 |
| Number of Pages | 15 |
| Year | 2021 |
| Publisher | MDPI AG |
| Place of Publication | Switzerland |
| ISSN | 2079-9292 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/electronics10141740 |
| Web Address (URL) | https://www.mdpi.com/2079-9292/10/14/1740 |
| Abstract | Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead to misdiagnoses. Hence, noninvasive methods such as electroencephalographic (EEG) recordings of PD patients can be an alternative biomarker. In this study, a deep-learning model is proposed for automated PD diagnosis. EEG recordings of 16 healthy controls and 15 PD patients were used for analysis. Using Gabor transform, EEG recordings were converted into spectrograms, which were used to train the proposed two-dimensional convolutional neural network (2D-CNN) model. As a result, the proposed model achieved high classification accuracy of 99.46% (±0.73) for 3-class classification (healthy controls, and PD patients with and without medication) using tenfold cross-validation. This indicates the potential of proposed model to simultaneously automatically detect PD patients and their medication status. The proposed model is ready to be validated with a larger database before implementation as a computer-aided diagnostic (CAD) tool for clinical-decision support. |
| Keywords | Classification; Parkinson’s disease (PD); electroencephalogram (EEG); deep learning; CNN; Gabor transform; spectrograms |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | Singapore University of Social Sciences (SUSS), Singapore |
| Sydney Children's Hospital, Australia | |
| University of New South Wales | |
| University of Technology Sydney | |
| School of Management and Enterprise | |
| Firat University, Turkey | |
| Ardahan University, Turkiye | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan | |
| Kumamoto University, Japan |
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