University of Oulu

A. Leino et al., "Deep Learning Enables Accurate Automatic Sleep Staging Based on Ambulatory Forehead EEG," in IEEE Access, vol. 10, pp. 26554-26566, 2022, doi: 10.1109/ACCESS.2022.3154899

Deep learning enables accurate automatic sleep staging based on ambulatory forehead EEG

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Author: Leino, Akseli1,2; Korkalainen, Henri1,2; Kalevo, Laura1,2;
Organizations: 1Department of Applied Physics, University of Eastern Finland, 70211 Kuopio, Finland
2Diagnostic Imaging Center, Kuopio University Hospital, 70210 Kuopio, Finland
3Sleep Disorders Centre, Department of Respiratory and Sleep Medicine, Princess Alexandra Hospital, Brisbane, QLD 4102, Australia
4Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD 4000, Australia
5Research Unit of Oral Health Sciences, University of Oulu, 90570 Oulu, Finland
6Medical Research Center, Oulu University Hospital, University of Oulu, 90570 Oulu, Finland
7Department of Oral and Maxillofacial Diseases, University of Helsinki, 00100 Helsinki, Finland
8Department of Otorhinolaryngology, Kuopio University Hospital, 70210 Kuopio, Finland
9Department of Oncology, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland
10School of Medicine, Institute of Clinical Medicine, University of Eastern Finland, 70211 Kuopio, Finland
11Science Service Center, Kuopio University Hospital, 70210 Kuopio, Finland
12School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.9 MB)
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Language: English
Published: Institute of Electrical and Electronics Engineers, 2022
Publish Date: 2022-05-10


We have previously developed an ambulatory electrode set (AES) for the measurement of electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG). The AES has been proven to be suitable for manual sleep staging and self-application in in- home polysomnography (PSG). To further facilitate the diagnostics of various sleep disorders, this study aimed to utilize a deep learning-based automated sleep staging approach for EEG signals acquired with the AES. The present neural network architecture comprises a combination of convolutional and recurrent neural networks previously shown to achieve excellent sleep scoring accuracy with a single standard EEG channel (F4-M1). In this study, the model was re- trained and tested with 135 EEG signals recorded with AES. The recordings were conducted for subjects suspected of sleep apnea or sleep bruxism. The performance of the deep learning model was evaluated with 10-fold cross-validation using manual scoring of the AES signals as a reference. The accuracy of the neural network sleep staging was 79.7% (κ = 0.729 ) for five sleep stages (W, N1, N2, N3, and R), 84.1% (κ = 0.773 ) for four sleep stages (W, light sleep, deep sleep, R), and 89.1% (κ = 0.801 ) for three sleep stages (W, NREM, R). The utilized neural network was able to accurately determine sleep stages based on EEG channels measured with the AES. The accuracy is comparable to the inter-scorer agreement of standard EEG scorings between international sleep centers. The automatic AES-based sleep staging could potentially improve the availability of PSG studies by facilitating the arrangement of self-administrated in- home PSGs.

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Series: IEEE access
ISSN: 2169-3536
ISSN-E: 2169-3536
ISSN-L: 2169-3536
Volume: 10
Pages: 26554 - 26566
DOI: 10.1109/ACCESS.2022.3154899
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
Funding: This work was supported in part by the EU Commission Horizon 2020 Framework Program under Grant Agreement 965417; in part by the NordForsk (NordSleep Project 90458-06111) through Business Finland under Grant 5133/31/2018; in part by the Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding under Grant 5041767, Grant 5041794, Grant 5041797, Grant 5041798, Grant 5041776, and Grant 5041803; in part by the Academy of Finland under Grant 323536; in part by The Research Foundation of the Pulmonary Diseases; in part by the Finnish Cultural Foundation North Savo Regional Fund; in part by the Finnish Cultural Foundation; in part by the Päivikki and Sakari Sohlberg Foundation; in part by the Finnish Anti-Tuberculosis Association; in part by the Tampere Tuberculosis Foundation; and in part by the Kuopio Area Respiratory Foundation.
Copyright information: © The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see