Neurological Status Classification Using Convolutional Neural Network

• URL: http://arxiv.org/abs/2104.02058v1
• Date: Thu, 1 Apr 2021 22:40:28 GMT
• Title: Neurological Status Classification Using Convolutional Neural Network
• Authors: Mehrad Jaloli, Divya Choudhary and Marzia Cescon
• Abstract summary: We show that a Convolutional Neural Network (CNN) model is able to accuratelydiscriminate between 4 different phases of neurological status. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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