  In this study we show that a Convolutional Neural Network (CNN) model is able
to accuratelydiscriminate between 4 different phases of neurological status in
a non-Electroencephalogram(EEG) dataset recorded in an experiment in which
subjects are exposed to physical, cognitiveand emotional stress. We demonstrate
that the proposed model is able to obtain 99.99% AreaUnder the Curve (AUC) of
Receiver Operation characteristic (ROC) and 99.82% classificationaccuracy on
the test dataset. Furthermore, for comparison, we show that our models
outperformstraditional classification methods such as SVM, and RF. Finally, we
show the advantage of CNN models, in comparison to other methods, in robustness
to noise by 97.46% accuracy on a noisy dataset.
