Please use this identifier to cite or link to this item:
Title: CoSleepNet: Automated sleep staging using a hybrid CNN-LSTM network on imbalanced EEG-EOG datasets
Authors: Efe, Enes
Özşen, Seral
Keywords: Automatic sleep staging
Discrete cosine transform
Focal loss
Issue Date: 2023
Publisher: Elsevier Sci Ltd
Abstract: Sleep relaxes and rests the body by slowing down the metabolism, making us physically stronger and fitter when we wake up. However, in a sleep disorder that may occur in humans, this process is reversed and various dis-orders occur in the body. Therefore, determining sleep stages is vital for diagnosing and treating such sleep disorders. However, manual scoring of sleep stages is tedious, time-consuming and requires considerable expertise. It also suffers from inter-observer variability. Deep learning techniques can automate this process, overcome these problems and produce more consistent results. This study proposes a new hybrid neural network architecture using focal loss and discrete cosine transform methods to solve the training data imbalance problem. The model was trained on four different databases using k-fold validation strategies (subject-wise), and the highest score was 87.11% accuracy, 81.81% Kappa score, and 79.83% MF1 when using two channels (EEG-EOG). The results of our approach are promising when compared to existing methods.
ISSN: 1746-8094
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

Files in This Item:
File SizeFormat 
  Until 2030-01-01
1.08 MBAdobe PDFView/Open    Request a copy
Show full item record

CORE Recommender

Page view(s)

checked on Mar 20, 2023

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.