@inproceedings{42524a638dec4f6c8fc7547d91c231e6,
title = "An Ensemble Deep Learning Approach for Sleep Stage Classification via Single-channel EEG and EOG",
abstract = "Classification of sleep stages is important for diagnosis and treatment of sleep disorder. Manual classification performed by sleep experts is burdensome and time-consuming. This study proposes a novel model for sleep stage classification. EEG and EOG signals of 153 healthy subjects was used. The proposed model ensembles two EEGNet-BiLSTM models which learn EEG and EOG respectively. Compared to the existing models, the two models yielded approximately 82% accuracy and 0.78 k-value, whereas the proposed ensemble model showed 90% accuracy and 0.80 k-value. The proposed ensemble model is superior in terms of accuracy and consistency compared to the conventional models. ",
keywords = "classification, deep learning, electrocardiogram, electroencephalography, ensemble, Sleep stages",
author = "Wang, {In Nea} and Lee, {Choel Hui} and Kim, {Hyun Ji} and Hakseung Kim and Kim, {Dong Joo}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2020 Elsevier B.V., All rights reserved.; 11th International Conference on Information and Communication Technology Convergence, ICTC 2020 ; Conference date: 21-10-2020 Through 23-10-2020",
year = "2020",
month = oct,
day = "21",
doi = "10.1109/ICTC49870.2020.9289335",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "394--398",
booktitle = "ICTC 2020 - 11th International Conference on ICT Convergence",
}