A novel automated Parkinson's disease identification approach using deep learning and EEG

Marwa Obayya; Muhammad Kashif Saeed; Mashael Maashi; Saud S Alotaibi; Ahmed S Salama; Manar Ahmed Hamza

doi:10.7717/peerj-cs.1663

A novel automated Parkinson's disease identification approach using deep learning and EEG

PeerJ Comput Sci. 2023 Nov 22:9:e1663. doi: 10.7717/peerj-cs.1663. eCollection 2023.

Authors

Marwa Obayya¹, Muhammad Kashif Saeed², Mashael Maashi³, Saud S Alotaibi⁴, Ahmed S Salama⁵, Manar Ahmed Hamza⁶

Affiliations

¹ Department of Biomedical Engineering, College of Engineering, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
² Department of Computer Science, King Khalid University, Abha, Saudi Arabia.
³ Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
⁴ Department of Information Systems, Umm Al-Qura University, Mecca, Saudi Arabia.
⁵ Department of Electrical Engineering, Future University in Egypt, New Cairo, New Cairo, Egypt.
⁶ Department of Computer and Self Development, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia.

Abstract

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.

Keywords: Clinical diagnostics; Deep learning; Electroencephalography; Gabor transform; Parkinson’s disease.

Grants and funding

This study was funded by the Deanship of Scientific Research at King Khalid University through large group Research Project under grant number (RGP2/117/44), Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R203), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. Research Supporting Project number(RSPD2023R787), King Saud University, Riyadh, Saudi Arabia, Prince Sattam bin Abdulaziz University project number (PSAU/2023/R/1444), and by the Future University in Egypt (FUE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.