A Time Series Approach to Parkinson’s Disease Classification from EEG

Firstly, we present a novel representation for EEG data,
a 7-variate series of band power coefficients, which en-
ables the use of (previously inaccessible) time series clas-
sification methods. Specifically, we implement the multi-
resolution representation-based time series classification
method MrSQL. This is deployed on a challenging early-
stage Parkinson’s dataset that includes wakeful and sleep
EEG. Initial results are promising with over 90% accuracy
achieved on all EEG data types used. Secondly, we present a
framework that enables high-importance data types and brain
regions for classification to be identified. Using our frame-
work, we find that, across different EEG data types, it is the
Prefrontal brain region that has the most predictive power
for the presence of Parkinson’s Disease. This outperformance
was statistically significant versus ten of the twelve other
brain regions (not significant versus adjacent Left Frontal and
Right Frontal regions). The Prefrontal region of the brain is
important for higher-order cognitive processes and our results
align with studies that have shown neural dysfunction in the
prefrontal cortex in Parkinson’s Disease.