Citation

Hasib, Tanvir and Vengadasalam, V Vijayakumar and Kannan, Ramakrishnan (2025) Early Identification of Parkinson's Disease Using Time Frequency Analysis on EEG Signals. Journal of Informatics and Web Engineering, 4 (1). pp. 168-183. ISSN 2821-370X

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Abstract

Parkinson's Disease (PD) is a progressive neurological disorder. Itaffects movement and can significantly impact quality of life. Early and accurate diagnosis is crucial for effective management and intervention. Traditional diagnostic methods can be time-consuming and less effective in the early stages of the disease.This study aims to develop an automated approach for identifying PD using time-frequency image analysis of electroencephalogram (EEG) signals. The goal is to enhance diagnostic accuracy and efficiency, facilitating early detection.EEG signals, often contaminated with artifacts such as eye blinks and muscle movementsetc., were first cleaned. Time-frequency images were then plotted from the cleaned signals, and Event-Related Spectral Perturbation (ERSP) plots were extracted. A customized deep learning model was employed to classify the ERSP plots, distinguishing PD patients from healthy controls.The deep learning model achieved an accuracy of 94.64% in separating PD patients from healthy controls. The approach demonstrated robustness against common EEG artifacts, ensuring reliable PD detection. The model's architecture was specifically designed tohandle the complexities of EEG data, making it a powerful tool for PD classifications.This study highlights the potential of integrating deep learning with EEG analysis to explorePD diagnosis. The proposed method is faster and more accurate than traditional approaches, enabling early detection and timely intervention. By reducing the time required for analysis and enhancing diagnostic accuracy, this approach can significantly improve patient outcomes and support better management of Parkinson's Disease.

Item Type:	Article
Uncontrolled Keywords:	Parkinson's Diseas
Subjects:	R Medicine > RC Internal medicine
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	25 Jun 2025 06:45
Last Modified:	25 Jun 2025 06:45
URII:	http://shdl.mmu.edu.my/id/eprint/13992

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