  We evaluate the effectiveness of combining brain connectivity metrics with
signal statistics for early stage Parkinson's Disease (PD) classification using
electroencephalogram data (EEG). The data is from 5 arousal states - wakeful
and four sleep stages (N1, N2, N3 and REM). Our pipeline uses an Ada Boost
model for classification on a challenging early stage PD classification task
with with only 30 participants (11 PD , 19 Healthy Control). Evaluating 9 brain
connectivity metrics we find the best connectivity metric to be different for
each arousal state with Phase Lag Index achieving the highest individual
classification accuracy of 86\% on N1 data. Further to this our pipeline using
regional signal statistics achieves an accuracy of 78\%, using brain
connectivity only achieves an accuracy of 86\% whereas combining the two
achieves a best accuracy of 91\%. This best performance is achieved on N1 data
using Phase Lag Index (PLI) combined with statistics derived from the frequency
characteristics of the EEG signal. This model also achieves a recall of 80 \%
and precision of 96\%. Furthermore we find that on data from each arousal
state, combining PLI with regional signal statistics improves classification
accuracy versus using signal statistics or brain connectivity alone. Thus we
conclude that combining brain connectivity statistics with regional EEG
statistics is optimal for classifier performance on early stage Parkinson's.
Additionally, we find outperformance of N1 EEG for classification of
Parkinson's and expect this could be due to disrupted N1 sleep in PD. This
should be explored in future work.
