Parkinson's disease is a chronic disease characterised by the deterioration of neurons and a consequent deficiency of the neurotransmitter dopamine. Parkinson's disease can make patient's life very difficult, since it causes a wide range of symptoms and problems that significantly impair quality of life. It is therefore crucial to detect and treat the disease as early as possible to alleviate symptoms.
In the past, the analysis of electroencephalographic (EEG) signals has emerged as one of the potential diagnostic methods for Parkinson's disease. In our work, we tried to distinguish between EEG recordings of healthy subjects and subjects with Parkinson's disease. For our study, we used a database of EEG recordings from 25 Parkinson's patients and 25 control subjects. We approached the problem by using the signals from each electrode of the individual recordings, processing them with different methods, visualising them and then identified signals where the differences are the most obvious. We then analysed all the recordings from the database, creating pooled pairs of corresponding control subjects and Parkinson's patients who had not received medication for 15 hours, and Parkinson's patients who were taking medication regularly. In our case, the most informative signals were those recorded at frontal electrodes F3 to F8. Neural Networks, Quadratic Discriminant Analysis and k-Nearest Neighbors were the most successful for classification. We chose the whole frequency range because the best classification results were obtained there. The classification accuracy on the test set was 93% using the Neural Networks, 89.5% using the Quadratic Discriminant Analysis, and 87.7% using the k-Nearest Neighbors classifier.
The results of our study suggest that discriminating between Parkinson's disease and healthy subjects based on the analysis of EEG recordings is possible already at an early stage. The results of our study are also better than previously published classification results on the dataset we used.
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