  Detecting Parkinson's Disease in its early stages using EEG data presents a
significant challenge. This paper introduces a novel approach, representing EEG
data as a 15-variate series of bandpower and peak frequency
values/coefficients. The hypothesis is that this representation captures
essential information from the noisy EEG signal, improving disease detection.
Statistical features extracted from this representation are utilised as input
for interpretable machine learning models, specifically Decision Tree and
AdaBoost classifiers. Our classification pipeline is deployed within our
proposed framework which enables high-importance data types and brain regions
for classification to be identified. Interestingly, our analysis reveals that
while there is no significant regional importance, the N1 sleep data type
exhibits statistically significant predictive power (p < 0.01) for early-stage
Parkinson's Disease classification. AdaBoost classifiers trained on the N1 data
type consistently outperform baseline models, achieving over 80% accuracy and
recall. Our classification pipeline statistically significantly outperforms
baseline models indicating that the model has acquired useful information.
Paired with the interpretability (ability to view feature importance's) of our
pipeline this enables us to generate meaningful insights into the
classification of early stage Parkinson's with our N1 models. In Future, these
models could be deployed in the real world - the results presented in this
paper indicate that more than 3 in 4 early-stage Parkinson's cases would be
captured with our pipeline.
