Neurological Status Classification Using Convolutional Neural Network

TL;DR

This paper demonstrates that a CNN model can accurately classify four neurological status phases from EEG data, outperforming traditional methods and showing robustness to noise.

Contribution

The study introduces a CNN-based approach for neurological status classification that achieves high accuracy and noise robustness, surpassing traditional classifiers.

Findings

99.99% AUC on test data

Outperforms SVM and RF classifiers

97.46% accuracy on noisy data

Abstract

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.