Automatic Sleep Stage Classification by CNN-Transformer-LSTM using single-channel EEG signal
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43970947" target="_blank" >RIV/49777513:23520/23:43970947 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/10385687" target="_blank" >https://ieeexplore.ieee.org/document/10385687</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/BIBM58861.2023.10385687" target="_blank" >10.1109/BIBM58861.2023.10385687</a>
Alternative languages
Result language
angličtina
Original language name
Automatic Sleep Stage Classification by CNN-Transformer-LSTM using single-channel EEG signal
Original language description
Sleep stage classification plays a crucial role in diagnosing sleep disorders and understanding sleep physiology. In recent years, automated models based on machine learning and deep learning have gained attention for sleep stage classification. This paper uses the single-channel EEG signal to present an automatic sleep stage classification system using a combination of Convolutional Neural Network (CNN), Transformer, and Long Short-Term Memory (LSTM) models. Experimental evaluation of the ISRUC sleep datasets S1 and S3 demonstrates the effectiveness of the proposed model. It achieves accuracies of 80.37% and 82.40%, respectively, achieving competitive performance compared to state-of-the-art models.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
ISBN
979-8-3503-3748-8
ISSN
2156-1125
e-ISSN
2156-1133
Number of pages
5
Pages from-to
2559-2563
Publisher name
IEEE
Place of publication
Piscataway
Event location
Istanbul
Event date
Dec 5, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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