A novel automated Parkinson’s disease identification approach using deep learning and EEG
The neurological ailment known as Parkinson’s disease (PD) affects people throughout the globe. The neurodegenerative PD-related disorder primarily affects people in middle to late life. Motor symptoms such as tremors, muscle rigidity, and sluggish, clumsy movement are common in patients with this d...
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| Format: | Article |
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PeerJ Inc.
2023-11-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-1663.pdf |
| _version_ | 1827638853626757120 |
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| author | Marwa Obayya Muhammad Kashif Saeed Mashael Maashi Saud S. Alotaibi Ahmed S. Salama Manar Ahmed Hamza |
| author_facet | Marwa Obayya Muhammad Kashif Saeed Mashael Maashi Saud S. Alotaibi Ahmed S. Salama Manar Ahmed Hamza |
| author_sort | Marwa Obayya |
| collection | DOAJ |
| description | The neurological ailment known as Parkinson’s disease (PD) affects people throughout the globe. The neurodegenerative PD-related disorder primarily affects people in middle to late life. Motor symptoms such as tremors, muscle rigidity, and sluggish, clumsy movement are common in patients with this disorder. Genetic and environmental variables play significant roles in the development of PD. Despite much investigation, the root cause of this neurodegenerative disease is still unidentified. Clinical diagnostics rely heavily on promptly detecting such irregularities to slow or stop the progression of illnesses successfully. Because of its direct correlation with brain activity, electroencephalography (EEG) is an essential PD diagnostic technique. Electroencephalography, or EEG, data are biomarkers of brain activity changes. However, these signals are non-linear, non-stationary, and complicated, making analysis difficult. One must often resort to a lengthy human labor process to accomplish results using traditional machine-learning approaches. The breakdown, feature extraction, and classification processes are typical examples of these stages. To overcome these obstacles, we present a novel deep-learning model for the automated identification of Parkinson’s disease (PD). The Gabor transform, a standard method in EEG signal processing, was used to turn the raw data from the EEG recordings into spectrograms. In this research, we propose densely linked bidirectional long short-term memory (DLBLSTM), which first represents each layer as the sum of its hidden state plus the hidden states of all layers above it, then recursively transmits that representation to all layers below it. This study’s suggested deep learning model was trained using these spectrograms as input data. Using a robust sixfold cross-validation method, the proposed model showed excellent accuracy with a classification accuracy of 99.6%. The results indicate that the suggested algorithm can automatically identify PD. |
| first_indexed | 2024-03-09T16:28:38Z |
| format | Article |
| id | doaj.art-a27a8424c0004a22adaefff37fb17c23 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-09T16:28:38Z |
| publishDate | 2023-11-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-a27a8424c0004a22adaefff37fb17c232023-11-24T15:05:09ZengPeerJ Inc.PeerJ Computer Science2376-59922023-11-019e166310.7717/peerj-cs.1663A novel automated Parkinson’s disease identification approach using deep learning and EEGMarwa Obayya0Muhammad Kashif Saeed1Mashael Maashi2Saud S. Alotaibi3Ahmed S. Salama4Manar Ahmed Hamza5Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Computer Science, King Khalid University, Abha, Saudi ArabiaDepartment of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Information Systems, Umm Al-Qura University, Mecca, Saudi ArabiaDepartment of Electrical Engineering, Future University in Egypt, New Cairo, New Cairo, EgyptDepartment of Computer and Self Development, Prince Sattam bin Abdulaziz University, AlKharj, Saudi ArabiaThe neurological ailment known as Parkinson’s disease (PD) affects people throughout the globe. The neurodegenerative PD-related disorder primarily affects people in middle to late life. Motor symptoms such as tremors, muscle rigidity, and sluggish, clumsy movement are common in patients with this disorder. Genetic and environmental variables play significant roles in the development of PD. Despite much investigation, the root cause of this neurodegenerative disease is still unidentified. Clinical diagnostics rely heavily on promptly detecting such irregularities to slow or stop the progression of illnesses successfully. Because of its direct correlation with brain activity, electroencephalography (EEG) is an essential PD diagnostic technique. Electroencephalography, or EEG, data are biomarkers of brain activity changes. However, these signals are non-linear, non-stationary, and complicated, making analysis difficult. One must often resort to a lengthy human labor process to accomplish results using traditional machine-learning approaches. The breakdown, feature extraction, and classification processes are typical examples of these stages. To overcome these obstacles, we present a novel deep-learning model for the automated identification of Parkinson’s disease (PD). The Gabor transform, a standard method in EEG signal processing, was used to turn the raw data from the EEG recordings into spectrograms. In this research, we propose densely linked bidirectional long short-term memory (DLBLSTM), which first represents each layer as the sum of its hidden state plus the hidden states of all layers above it, then recursively transmits that representation to all layers below it. This study’s suggested deep learning model was trained using these spectrograms as input data. Using a robust sixfold cross-validation method, the proposed model showed excellent accuracy with a classification accuracy of 99.6%. The results indicate that the suggested algorithm can automatically identify PD.https://peerj.com/articles/cs-1663.pdfClinical diagnosticsDeep learningElectroencephalographyGabor transformParkinson’s disease |
| spellingShingle | Marwa Obayya Muhammad Kashif Saeed Mashael Maashi Saud S. Alotaibi Ahmed S. Salama Manar Ahmed Hamza A novel automated Parkinson’s disease identification approach using deep learning and EEG PeerJ Computer Science Clinical diagnostics Deep learning Electroencephalography Gabor transform Parkinson’s disease |
| title | A novel automated Parkinson’s disease identification approach using deep learning and EEG |
| title_full | A novel automated Parkinson’s disease identification approach using deep learning and EEG |
| title_fullStr | A novel automated Parkinson’s disease identification approach using deep learning and EEG |
| title_full_unstemmed | A novel automated Parkinson’s disease identification approach using deep learning and EEG |
| title_short | A novel automated Parkinson’s disease identification approach using deep learning and EEG |
| title_sort | novel automated parkinson s disease identification approach using deep learning and eeg |
| topic | Clinical diagnostics Deep learning Electroencephalography Gabor transform Parkinson’s disease |
| url | https://peerj.com/articles/cs-1663.pdf |
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