文章基本信息

标题：Neurological Status Classification Using Convolutional Neural Network
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作者：Mehrad Jaloli ; Divya Choudhary ; Marzia Cescon 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2020
卷号：53
期号：5
页码：409-414
DOI：10.1016/j.ifacol.2021.04.193
语种：English
出版社：Elsevier
摘要：AbstractIn this study we show that a Convolutional Neural Network (CNN) model is able to accurately discriminate between 4 different phases of neurological status in a non-Electroencephalogram (EEG) dataset recorded in an experiment in which subjects are exposed to physical, cognitive and emotional stress. We demonstrate that the proposed model is able to obtain 99.99% Area Under the Curve (AUC) of Receiver Operation characteristic (ROC) and 99.82% classification accuracy on the test dataset. Furthermore, for comparison, we show that our models outperforms traditional 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.
关键词：KeywordsAssistive devicesCognitive controlPotential impact of automationopen problemsDeep neural networkPhysiological signal processingNeurological status assessment