Presenting a method for detecting Parkinson's disease from electroencephalography (EEG) signals using machine learning techniques

Sadatnoori, Seyed Ali; Golshahi, Hakim

doi:10.22075/mseee.2026.41427.1280

Presenting a method for detecting Parkinson's disease from electroencephalography (EEG) signals using machine learning techniques

Articles in Press

Document Type : Research Article

Authors

Seyed Ali Sadatnoori ¹

Hakim Golshahi ²

¹ Department of Electrical Engineering, Sho. C, Islamic Azad University, Shoushtar, Iran.

² Department of Physics, Sho. C, Islamic Azad University, Shoushtar, Iran.

10.22075/mseee.2026.41427.1280

Abstract

Parkinson's disease (PD) is an incurable neurological disorder caused by a decrease in dopamine levels in the human brain. This paper investigates and analyzes EEG signals from Parkinson's disease patients and healthy individuals. To simulate the proposed method, the Timing Interval PD database available on the UNM portal was used, which includes EEG signals from 37 healthy volunteers and 92 Parkinson's disease patients. The average age of the volunteers in the healthy group was 71.3 years and in the patient group was 68.7 years. EEG signals were recorded with a sampling frequency of 500 Hz and 63 channels. In order to increase the processing quality, the signals have undergone preprocessing steps including filtering, noise removal, normalization, and segmentation. In the filtering step, a band-pass filter with a frequency range of 1 to 30 Hz has been used to remove additional noise. Also, the signals were normalized using the z-score normalization method and divided into ten-second segments. Four classification methods including Support Vector Machine (SVM), K-Nearest Neighbor (KNN) , Decision Tree (DT) and Random Forest (RF) have been used. The proposed method has achieved the best performance of 96.80. The performance of the models has been evaluated using accuracy, sensitivity and specificity indices.

Keywords

Electroencephalogram signal

machine learning

Parkinson's disease

feature reduction

classification

Subjects

Biomedical Engineering

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