Neurological Status Classification Using Convolutional Neural Network
This work addresses neurological monitoring under stress, but it is incremental as it applies an existing CNN method to a new dataset.
The study tackled the problem of classifying neurological status phases under stress using a Convolutional Neural Network (CNN) on a non-EEG dataset, achieving 99.99% AUC, 99.82% accuracy, and 97.46% accuracy on noisy data.
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.