The effect of artifact rejection on the performance of a convolutional neural network based algorithm for binary EEG data classification

The effect of artifact rejection on the performance of a convolutional neural network based algorithm for binary EEG data classification

Objective: Evaluate the effect of artifact rejection on the performance of a Convolutional Neural Network (CNN) based algorithm for classification of abnormal and normal electroencephalography (EEG) data. Methods: We developed an automated CNN-based clean versus artifact classification algorithm and...

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Bibliographic Details
Main Authors: Camps, C.R (Author), Coutinho, J.M (Author), Doelkahar, B.S (Author), Marquering, H.A (Author), Potters, W.V (Author), van Stigt, M.N (Author)
Format: Article
Language:English
Published: Elsevier Ltd 2023
Subjects:
article
artifact
Artifact
Artifact rejections
artificial intelligence
bootstrapping
Classification (of information)
classification algorithm
Classification algorithm
clinical practice
controlled study
Convolution
convolutional neural network
Convolutional neural network
Convolutional neural networks
cross validation
Cross validation
Diagnostic method
Diagnostic methods
diagnostic procedure
electroencephalogram
electroencephalography
Electroencephalography
Electrophysiology
Network-based
Network-based algorithm
Performance
recall
Test accuracy
validation process
velocity
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