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Somaskandhan P, Leppänen T, Terrill PI, Sigurdardottir S, Arnardottir ES, Ólafsdóttir KA, Serwatko M, Sigurðardóttir SÞ, Clausen M, Töyräs J, Korkalainen H. Deep learning-based algorithm accurately classifies sleep stages in preadolescent children with sleep-disordered breathing symptoms and age-matched controls. Front Neurol 2023; 14:1162998. [PMID: 37122306 PMCID: PMC10140398 DOI: 10.3389/fneur.2023.1162998] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 03/23/2023] [Indexed: 05/02/2023] Open
Abstract
Introduction Visual sleep scoring has several shortcomings, including inter-scorer inconsistency, which may adversely affect diagnostic decision-making. Although automatic sleep staging in adults has been extensively studied, it is uncertain whether such sophisticated algorithms generalize well to different pediatric age groups due to distinctive EEG characteristics. The preadolescent age group (10-13-year-olds) is relatively understudied, and thus, we aimed to develop an automatic deep learning-based sleep stage classifier specifically targeting this cohort. Methods A dataset (n = 115) containing polysomnographic recordings of Icelandic preadolescent children with sleep-disordered breathing (SDB) symptoms, and age and sex-matched controls was utilized. We developed a combined convolutional and long short-term memory neural network architecture relying on electroencephalography (F4-M1), electrooculography (E1-M2), and chin electromyography signals. Performance relative to human scoring was further evaluated by analyzing intra- and inter-rater agreements in a subset (n = 10) of data with repeat scoring from two manual scorers. Results The deep learning-based model achieved an overall cross-validated accuracy of 84.1% (Cohen's kappa κ = 0.78). There was no meaningful performance difference between SDB-symptomatic (n = 53) and control subgroups (n = 52) [83.9% (κ = 0.78) vs. 84.2% (κ = 0.78)]. The inter-rater reliability between manual scorers was 84.6% (κ = 0.78), and the automatic method reached similar agreements with scorers, 83.4% (κ = 0.76) and 82.7% (κ = 0.75). Conclusion The developed algorithm achieved high classification accuracy and substantial agreements with two manual scorers; the performance metrics compared favorably with typical inter-rater reliability between manual scorers and performance reported in previous studies. These suggest that our algorithm may facilitate less labor-intensive and reliable automatic sleep scoring in preadolescent children.
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Affiliation(s)
- Pranavan Somaskandhan
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
- *Correspondence: Pranavan Somaskandhan,
| | - Timo Leppänen
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
| | - Philip I. Terrill
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
| | - Sigridur Sigurdardottir
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Erna Sif Arnardottir
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
- Internal Medicine Services, Landspitali–The National University Hospital of Iceland, Reykjavik, Iceland
| | - Kristín A. Ólafsdóttir
- Reykjavik University Sleep Institute, School of Technology, Reykjavik University, Reykjavik, Iceland
| | - Marta Serwatko
- Department of Clinical Engineering, Landspitali University Hospital, Reykjavik, Iceland
| | - Sigurveig Þ. Sigurðardóttir
- Department of Immunology, Landspitali University Hospital, Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
| | - Michael Clausen
- Department of Allergy, Landspitali University Hospital, Reykjavik, Iceland
- Children's Hospital Reykjavik, Reykjavik, Iceland
| | - Juha Töyräs
- School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD, Australia
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Science Service Center, Kuopio University Hospital, Kuopio, Finland
| | - Henri Korkalainen
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
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Arnardottir ES, Islind AS, Óskarsdóttir M, Ólafsdóttir KA, August E, Jónasdóttir L, Hrubos-Strøm H, Saavedra JM, Grote L, Hedner J, Höskuldsson S, Ágústsson JS, Jóhannsdóttir KR, McNicholas WT, Pevernagie D, Sund R, Töyräs J, Leppänen T. The Sleep Revolution project: the concept and objectives. J Sleep Res 2022; 31:e13630. [PMID: 35770626 DOI: 10.1111/jsr.13630] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 04/19/2022] [Accepted: 04/19/2022] [Indexed: 12/18/2022]
Abstract
Obstructive sleep apnea is linked to severe health consequences such as hypertension, daytime sleepiness, and cardiovascular disease. Nearly a billion people are estimated to have obstructive sleep apnea with a substantial economic burden. However, the current diagnostic parameter of obstructive sleep apnea, the apnea-hypopnea index, correlates poorly with related comorbidities and symptoms. Obstructive sleep apnea severity is measured by counting respiratory events, while other physiologically relevant consequences are ignored. Furthermore, as the clinical methods for analysing polysomnographic signals are outdated, laborious, and expensive, most patients with obstructive sleep apnea remain undiagnosed. Therefore, more personalised diagnostic approaches are urgently needed. The Sleep Revolution, funded by the European Union's Horizon 2020 Research and Innovation Programme, aims to tackle these shortcomings by developing machine learning tools to better estimate obstructive sleep apnea severity and phenotypes. This allows for improved personalised treatment options, including increased patient participation. Also, implementing these tools will alleviate the costs and increase the availability of sleep studies by decreasing manual scoring labour. Finally, the project aims to design a digital platform that functions as a bridge between researchers, patients, and clinicians, with an electronic sleep diary, objective cognitive tests, and questionnaires in a mobile application. These ambitious goals will be achieved through extensive collaboration between 39 centres, including expertise from sleep medicine, computer science, and industry and by utilising tens of thousands of retrospectively and prospectively collected sleep recordings. With the commitment of the European Sleep Research Society and Assembly of National Sleep Societies, the Sleep Revolution has the unique possibility to create new standardised guidelines for sleep medicine.
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Affiliation(s)
- Erna S Arnardottir
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Landspitali University Hospital, Reykjavik, Iceland
| | - Anna Sigridur Islind
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Department of Computer Science, Reykjavik University, Reykjavik, Iceland
| | - María Óskarsdóttir
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Department of Computer Science, Reykjavik University, Reykjavik, Iceland
| | | | - Elias August
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Department of Engineering, Reykjavik University, Reykjavik, Iceland
| | - Lára Jónasdóttir
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland
| | - Harald Hrubos-Strøm
- Department of Otorhinolaryngology, Akershus University Hospital, Lørenskog, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Jose M Saavedra
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Physical Activity, Physical Education, Sport and Health (PAPESH) Research Group, Department of Sports Science, Reykjavik University, Reykjavik, Iceland
| | - Ludger Grote
- Internal Medicine, Center for Sleep and Wake Disorders, University of Gothenburg Sahlgrenska Academy, Gothenburg, Sweden
| | - Jan Hedner
- Internal Medicine, Center for Sleep and Wake Disorders, University of Gothenburg Sahlgrenska Academy, Gothenburg, Sweden
| | | | | | - Kamilla Rún Jóhannsdóttir
- Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.,Department of Psychology, Reykjavik University, Reykjavik, Iceland
| | - Walter T McNicholas
- Department of Respiratory and Sleep Medicine, St. Vincent's Hospital Group, School of Medicine, University College Dublin, Dublin, Ireland
| | - Dirk Pevernagie
- Respiratory Diseases, University Hospital Ghent, Ghent, Belgium.,Department of Internal Medicine and Paediatrics, Ghent University, Ghent, Belgium
| | - Reijo Sund
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
| | - Juha Töyräs
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.,Science Service Center, Kuopio University Hospital, Kuopio, Finland
| | - Timo Leppänen
- Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.,School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, Queensland, Australia.,Diagnostic Imaging Center, Kuopio University Hospital, Kuopio, Finland
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