Parkinsons Disease Detection via Resting-State Electroencephalography Using Signal Processing and Machine Learning Techniques
This work addresses the need for a consistent biomarker to monitor PD in clinical settings, though it is incremental as it applies existing signal processing and machine learning methods to a specific dataset.
The study tackled the problem of detecting Parkinson's Disease (PD) using resting-state EEG data from 15 PD patients and 16 healthy controls, achieving high classification metrics such as 97.5% accuracy and 0.975 AUC with a Random Forest model.
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.