EEG Classification by Minimalistic Convolutional Neural Network Utilizing Context Information

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21460%2F21%3A00353219" target="_blank" >RIV/68407700:21460/21:00353219 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1109/EHB52898.2021.9657572" target="_blank" >https://doi.org/10.1109/EHB52898.2021.9657572</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EHB52898.2021.9657572" target="_blank" >10.1109/EHB52898.2021.9657572</a>

Result language

angličtina

Original language name

EEG Classification by Minimalistic Convolutional Neural Network Utilizing Context Information

Original language description

Labelling of electroencephalography (EEG) or polysomnography (PSG) recordings can be time consuming for a physician (expert), especially if we consider long term monitoring (e.g. sleep stages). Convolutional neural networks (CNN), which use images created from raw or preprocessed data trials, appear to be a promising method for semi-automated label acquisition. To the best of our knowledge most of the existing studies use only one channel or a very small number of channels in combination with a complicated structure of the CNN. In our study we propose adding context information based on use of multiple channels in an input image. Based on our current understanding and state of the art we expect additional growth of final accuracy. Studies based on this approach show additional input from context information that enhances final validation accuracy. During our research we focused on small structure CNN with only three convolutional layers and two fully connected layers. Additionally, all methods which use CNN structure are heavily influenced by several parameters like number of classes or size of their convolution masks. Secondary goal of this study is to acquaint the reader with these parameters and with possible solutions for their problems. Based on more than six thousand different CNN settings and input settings we managed to acquire results up to 94.6% mean training accuracy and 88.2% mean validation accuracy in sleep stage classification on real PSG including sleep EEG recordings even with easy-to-create CNN structure.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

20601 - Medical engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

2021 International Conference on e-Health and Bioengineering (EHB)

ISBN

978-1-6654-4000-4

ISSN

2575-5137

e-ISSN

2575-5145

Number of pages

4

Pages from-to

1-4

Publisher name

IEEE Industrial Electronic Society

Place of publication

Vienna

Event location

Iasi

Event date

Nov 18, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000802227900036