Parkinsons Disease Detection via Resting-State Electroencephalography Using Signal Processing and Machine Learning Techniques

TL;DR

This study demonstrates that machine learning applied to EEG signals can accurately distinguish Parkinson's Disease patients from healthy controls, offering a promising biomarker for diagnosis and disease monitoring.

Contribution

The paper introduces a novel approach combining EEG signal processing and machine learning, achieving high accuracy in PD detection.

Findings

Random Forest achieved 97.5% accuracy

EEG features can effectively differentiate PD from healthy controls

Automated EEG analysis shows potential for clinical PD diagnosis

Abstract

Parkinsons Disease (PD) is a neurodegenerative disorder resulting in motor deficits due to advancing degeneration of dopaminergic neurons. PD patients report experiencing tremor, rigidity, visual impairment, bradykinesia, and several cognitive deficits. Although Electroencephalography (EEG) indicates abnormalities in PD patients, one major challenge is the lack of a consistent, accurate, and systemic biomarker for PD in order to closely monitor the disease with therapeutic treatments and medication. In this study, we collected Electroencephalographic data from 15 PD patients and 16 Healthy Controls (HC). We first preprocessed every EEG signal using several techniques and extracted relevant features using many feature extraction algorithms. Afterwards, we applied several machine learning algorithms to classify PD versus HC. We found the most significant metrics to be achieved by the Random Forest ensemble learning approach, with an accuracy, precision, recall, F1 score, and AUC of 97.5%, 100%, 95%, 0.967, and 0.975, respectively. The results of this study show promise for exposing PD abnormalities using EEG during clinical diagnosis, and automating this process using signal processing techniques and ML algorithms to evaluate the difference between healthy individuals and PD patients.