GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals
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. He...
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| Format: | Article |
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MDPI AG
2021-07-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/14/1740 |
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| author | Hui Wen Loh Chui Ping Ooi Elizabeth Palmer Prabal Datta Barua Sengul Dogan Turker Tuncer Mehmet Baygin U. Rajendra Acharya |
| author_facet | Hui Wen Loh Chui Ping Ooi Elizabeth Palmer Prabal Datta Barua Sengul Dogan Turker Tuncer Mehmet Baygin U. Rajendra Acharya |
| author_sort | Hui Wen Loh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T09:41:06Z |
| format | Article |
| id | doaj.art-65fccdab608049c08720960c8c0f09cb |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T09:41:06Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-65fccdab608049c08720960c8c0f09cb2023-11-22T03:39:23ZengMDPI AGElectronics2079-92922021-07-011014174010.3390/electronics10141740GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG SignalsHui Wen Loh0Chui Ping Ooi1Elizabeth Palmer2Prabal Datta Barua3Sengul Dogan4Turker Tuncer5Mehmet Baygin6U. Rajendra Acharya7School of Science and Technology, Singapore University of Social Sciences, Clementi 599494, SingaporeSchool of Science and Technology, Singapore University of Social Sciences, Clementi 599494, SingaporeCentre of Clinical Genetics, Sydney Children’s Hospitals Network, Randwick 2031, AustraliaFaculty of Engineering and Information Technology, University of Technology Sydney, Sydney 2007, AustraliaDepartment of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, TurkeyDepartment of Digital Forensics Engineering, College of Technology, Firat University, Elazig 23119, TurkeyDepartment of Computer Engineering, Faculty of Engineering, Ardahan University, Ardahan 75000, TurkeySchool of Science and Technology, Singapore University of Social Sciences, Clementi 599494, SingaporeParkinson’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.https://www.mdpi.com/2079-9292/10/14/1740Parkinson’s disease (PD)classificationelectroencephalogram (EEG)deep learningCNNGabor transform |
| spellingShingle | Hui Wen Loh Chui Ping Ooi Elizabeth Palmer Prabal Datta Barua Sengul Dogan Turker Tuncer Mehmet Baygin U. Rajendra Acharya GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals Electronics Parkinson’s disease (PD) classification electroencephalogram (EEG) deep learning CNN Gabor transform |
| title | GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals |
| title_full | GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals |
| title_fullStr | GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals |
| title_full_unstemmed | GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals |
| title_short | GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals |
| title_sort | gaborpdnet gabor transformation and deep neural network for parkinson s disease detection using eeg signals |
| topic | Parkinson’s disease (PD) classification electroencephalogram (EEG) deep learning CNN Gabor transform |
| url | https://www.mdpi.com/2079-9292/10/14/1740 |
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