Sleep Revolution

The Sleep Revolution project, funded by the European Union’s Horizon 2020, is introducing an approach based on machine learning to assess OSA severity and treatment needs. Through a set of wearables and a digital management platform, the project aims to bring together researchers, patients and healthcare professionals to provide a state of the art method of accurate OSA diagnosis and optimal treatment.

ESRS Involvement

The ESRS is responsible for Work Package 12: Standardisation and Dissemination. This involves facilitating the co-ordination and dissemination of findings from the many research projects carried out by the Sleep Revolution consortium. This will be achieved by presentations at the bi-annual congress of ESRS, which is attended by about 2,000 participants, and by publications in the society journal, the Journal of Sleep Research, which is one of the highest impact sleep journals worldwide. Work Package 12 will also include the employment of a research methodologist by ESRS, funded by Sleep Revolution, whose role will be to facilitate systematic reviews and meta-analyses of existing publications relating to relevant topics for the project. This work will help clarify the existing evidence base for relevant research topics and to identify relevant topics for further research.

Publications

Pitkänen, H., Nikkonen, S., Rissanen, M., Islind, A. S., Gretarsdottir, H., Arnardottir, E. S., Leppänen, T., & Korkalainen, H. (2023). Multi-centre arousal scoring agreement in the Sleep Revolution. Journal of Sleep Research, e14127.
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Biedebach, L., Rusanen, M., Þórðarson, B., Arnadóttir, E. S., Óskarsdóttir, M., Nikkonen, S., … & Islind, A. S. (2023). Towards a Deeper Understanding of Sleep Stages through their Representation in the Latent Space of Variational Autoencoders. 56th Hawaii International Conference on System Sciences, 3111-3121.
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Piccini, J., August, E., Óskarsdóttir, M., Arnardóttir, E.S. (2023). Using the Electrodermal Activity Signal and Machine Learning for Diagnosing Sleep . Frontiers in Sleep, 2:1127697.
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Pires, G. N., Arnardóttir, E. S., Islind, A. S., Leppänen, T., & McNicholas, W. T. (2023). Consumer sleep technology for the screening of obstructive sleep apnea and snoring: current status and a protocol for a systematic review and meta‐analysis of diagnostic test accuracy. Journal of Sleep Research, e13819.
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Rusanen, M., Huttunen, R., Korkalainen, H., Myllymaa, S., Töyräs, J., Myllymaa, K., … & Kainulainen, S. (2023). Generalizable Deep Learning-based Sleep Staging Approach for Ambulatory Textile Electrode Headband Recordings. IEEE Journal of Biomedical and Health Informatics.
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Sturludóttir, J.E., Sigurðardóttir, S., Serwatko, M., Arnardóttir, E.S., Hrubos-Strøm, H., Clausen, M.V., Sigurðardóttir, S., Óskarsdóttir, M. and Islind, A.S., Deep learning for sleep analysis on children with sleep-disordered breathing: Automatic detection of mouth breathing events. Frontiers in Sleep, 2, p.3.
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Sveinbjarnarson, B. F., Schmitz, L., Arnardottir, E. S. & Islind, A. S. (2023). The Sleep Revolution Platform: a Dynamic Data Source Pipeline and Digital Platform Architecture for Complex Sleep Data. Current Sleep Medicine Reports. 1-10.
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Þórðarson, B., Islind, A. S., Arnadóttir, E. S., & Óskarsdóttir, M. (2023).Exploration of Sleep Events in the Latent Space of Variational Autoencoders on a Breath-by-Breath Basis. 56th Hawaii International Conference on System Sciences, 3091-3101.
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Arnardottir, E. S., Islind, A. S., Óskarsdóttir, M., Ólafsdóttir, K. A., August, E., Jónasdóttir, L., … & Sleep Revolution. (2022). The Sleep Revolution project: the concept and objectives. Journal of Sleep Research, 31(4), e13630.
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Borsky, M., Serwatko, M., Arnardottir, E. S., & Mallett, J. (2022). Toward Sleep Study Automation: Detection Evaluation of Respiratory-Related Events. IEEE Journal of Biomedical and Health Informatics, 26(7), 3418-3426.
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Ebrahimian, S., Nahvi, A., Tashakori, M., Salmanzadeh, H., Mohseni, O., & Leppänen, T. (2022). Multi-Level Classification of Driver Drowsiness by Simultaneous Analysis of ECG and Respiration Signals Using Deep Neural Networks. International journal of environmental research and public health, 19(17), 10736.
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Ebrahimian, S., Sillanmäki, S., Hietakoste, S., Duce, B., Kulkas, A., Töyräs, J., … & Kainulainen, S. (2022). Inter-sleep stage variations in corrected QT interval differ between obstructive sleep apnea patients with and without stroke history. Plos one, 17(12).
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Hietakoste, S., Karhu, T., Sillanmäki, S., Bailón, R., Penzel, T., Töyräs, J., … & Kainulainen, S. (2022). Obstructive sleep apnoea-related respiratory events and desaturation severity are associated with the cardiac response. ERJ Open Research, 8(4).
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Huttunen, R., Leppänen, T., Duce, B., Arnardottir, E. S., Nikkonen, S., Myllymaa, S., … & Korkalainen, H. (2022). A Comparison of Signal Combinations for Deep Learning-Based Simultaneous Sleep Staging and Respiratory Event Detection. IEEE Transactions on Biomedical Engineering.
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Kalevo, L., Miettinen, T., Leino, A., Westeren-Punnonen, S., Sahlman, J., Mervaala, E., … & Myllymaa, K. (2022). Self-Applied Electrode Set Provides a Clinically Feasible Solution Enabling EEG Recording in Home Sleep Apnea Testing. IEEE Access, 10, 60633-60642.
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Karhu, T., Leppänen, T., Korkalainen, H., Myllymaa, S., Duce, B., Töyräs, J., & Nikkonen, S. (2022). Desaturation event scoring criteria affect the perceived severity of nocturnal hypoxic load. Sleep Medicine, 100, 479-486.
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Karhu, T., Leppänen, T., Töyräs, J., Oksenberg, A., Myllymaa, S., & Nikkonen, S. (2022). ABOSA–Freely available automatic blood oxygen saturation signal analysis software: Structure and validation. Computer Methods and Programs in Biomedicine, 226, 107120.
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Leino, A., Korkalainen, H., Kalevo, L., Nikkonen, S., Kainulainen, S., Ryan, A., … & Myllymaa, K. (2022). Deep learning enables accurate automatic sleep staging based on ambulatory forehead EEG. IEEE Access, 10, 26554-26566.
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Leppänen, T., Kainulainen, S., Korkalainen, H., Sillanmäki, S., Kulkas, A., Töyräs, J., & Nikkonen, S. (2022). Pulse Oximetry: The Working Principle, Signal Formation, and Applications. In Advances in the Diagnosis and Treatment of Sleep Apnea (pp. 205-218). Springer, Cham.
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Leppänen, T., Varon, C., de Zambotti, M., & Myllymaa, S. (2022). Machine Learning and Wearable Technology in Sleep Medicine. Frontiers in Digital Health, 4.
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Mendonça, F., Mostafa, S. S., Gupta, A., Arnardottir, E. S., Leppänen, T., Morgado-Dias, F., & Ravelo-García, A. G. (2022). A-phase index: an alternative view for sleep stability analysis based on automatic detection of the A-phases from the cyclic alternating pattern. Sleep.
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Nikkonen, S., Korkalainen, H., Töyräs, J., & Leppänen, T. (2022). STAR sleep recording export software for automatic export and anonymization of sleep studies. Scientific Reports, 12(1), 1-7.
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Oksenberg, A., & Leppänen, T. (2022). Duration of respiratory events in obstructive sleep apnea: Factors influencing the duration of respiratory events. Sleep Medicine Reviews, 101729.
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Oksenberg, A., & Leppänen, T. (2022). Duration of respiratory events in obstructive sleep apnea: In search of paradoxical results. Sleep Medicine Reviews, 101728.
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Pahari, P., Korkalainen, H., Karhu, T., Rissanen, M., Arnardottir, E. S., Hrubos‐Strøm, H., … & Nikkonen, S. (2022). Obstructive sleep apnea‐related intermittent hypoxaemia is associated with impaired vigilance. Journal of Sleep Research.
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Schmitz, L., Sveinbjarnarson, B. F., Gunnarsson, G. N, Davíðsson, Ó. A., Davíðsson, Þ. B., Arnardóttir, E. S., Óskarsdóttir, M. & Islind, A. S. (2022). Towards a Digital Sleep Diary Standard. In proceeding of 28th Americas Conference on Information Systems (AMCIS). Minneapolis, USA.
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Sigurdardottir, F. D., Øverby, C. T., Nikkonen, S., Karhu, T., Dammen, T., Nordhus, I. H., … & Hrubos‐Strøm, H. (2022). Novel oxygen desaturation parameters are associated with cardiac troponin I: Data from the Akershus Sleep Apnea Project. Journal of Sleep Research, e13581.
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Sillanmäki, S., Lipponen, J. A., Korkalainen, H., Kulkas, A., Leppänen, T., Nikkonen, S., … & Kainulainen, S. (2022). QTc prolongation is associated with severe desaturations in stroke patients with sleep apnea. BMC Pulmonary Medicine, 22(1), 1-10.
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Arnardottir, E. S., Islind, A. S., & Óskarsdóttir, M. (2021). The Future of Sleep Measurements: A Review and Perspective. Sleep Medicine Clinics, 16(3), 447-464.
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Arnardottir, E. S., Korkalainen, H., Nikkonen, S., Kainulainen, S., Dwivedi, A. K., Myllymaa, S., … & Töyräs, J. (2021). Preface: Improving Sleep Measurements for the Future xiii.
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Jóhannsdóttir, K. R., Ferretti, D., Árnadóttir, B. S., & Jónsdóttir, M. K. (2021). Objective Measures of Cognitive Performance in Sleep Disorder Research. Measuring Sleep, An Issue of Sleep Medicine Clinics, E-Book, 16(4), 575-593.
Link to publication

Korkalainen, H., Nikkonen, S., Kainulainen, S., Dwivedi, A. K., Myllymaa, S., Leppänen, T., & Töyräs, J. (2021). Self-Applied Home Sleep Recordings: The Future of Sleep Medicine. Sleep Medicine Clinics, 16(4), 545-556.
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Leppänen, T., Myllymaa, S., Kulkas, A., & Töyräs, J. (2021). Beyond the apnea–hypopnea index: alternative diagnostic parameters and machine learning solutions for estimation of sleep apnea severity. Sleep, 44(9).
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Pevernagie, D., Bauters, F. A., Hertegonne K. (2021). The Role of Patient-Reported Outcomes in Sleep Measurements. Sleep Medicine Clinics, Volume 16, Issue 4, Pages 595-606.
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Pevernagie, D. (2021). Future Treatment of Sleep Disorders: Syndromic Approach Versus Management of Treatable Traits? Sleep Medicine Clinics, Volume 16, Issue 3, Pages 465-473.
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Pitkänen, H., Duce, B., Leppänen, T., Kainulainen, S., Kulkas, A., Myllymaa, S., … & Korkalainen, H. (2021). Gamma power of electroencephalogram arousal is modulated by respiratory event type and severity in obstructive sleep apnea. IEEE Transactions on Biomedical Engineering, 69(4), 1417-1423.
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Rusanen, M., Kainulainen, S., Korkalainen, H., Kalevo, L., Myllymaa, K., Leppänen, T., … & Myllymaa, S. (2021). Technical Performance of Textile-Based Dry Forehead Electrodes Compared With Medical-Grade Overnight Home Sleep Recordings. IEEE Access, 9, 157902-157915.
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Rusanen, M., Myllymaa, S., Kalevo, L., Myllymaa, K., Töyräs, J., Leppänen, T., & Kainulainen, S. (2021). An in-laboratory comparison of FocusBand EEG device and textile electrodes against a medical-grade system and wet gel electrodes. IEEE Access, 9, 132580-132591.
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Schmitz, L., Sveinbjarnarson, B. F., Gunnarsson, G. N, Davíðsson, Ó. A., Davíðsson, Þ. B., Arnardóttir, E. S., Óskarsdóttir, M. & Islind, A. S. (2022). Towards a Digital Sleep Diary Standard. In proceeding of 28th Americas Conference on Information Systems (AMCIS). Minneapolis, USA.

Óskarsdóttir M., Islind A., August E., Arnardóttir E., Patou F., Maier A. (2022). Importance of Getting Enough Sleep and Daily Activity Data to Assess Variability: Longitudinal Observational Study JMIR Form Res 2022;6(2):e31807.

Jóhannsdóttir, K. R., Ferretti, D., Árnadóttir, B. S., & Jónsdóttir, M. K. (2021). Objective Measures of Cognitive Performance in Sleep Disorder Research. Measuring Sleep, An Issue of Sleep Medicine Clinics, E-Book16(4), 575-593.

Pevernagie, D., Bauters, F. A., Hertegonne K. (2021). The Role of Patient-Reported Outcomes in Sleep Measurements. Sleep Medicine Clinics, Volume 16, Issue 4, Pages 595-606.

Arnardottir, E. S., Korkalainen, H., Nikkonen, S., Kainulainen, S., Dwivedi, A. K., Myllymaa, S., … & Töyräs, J. (2021). Preface: Improving Sleep Measurements for the Future xiii.

Arnardottir, E. S., Islind, A. S., & Óskarsdóttir, M. (2021). The Future of Sleep Measurements: A Review and Perspective. Sleep Medicine Clinics16(3), 447-464.

Pevernagie, D. (2021). Future Treatment of Sleep Disorders: Syndromic Approach Versus Management of Treatable Traits? Sleep Medicine Clinics, Volume 16,  Issue 3, Pages 465-473.

Presentations

Two Sleep Revolution sessions were held at the 26th Congress of the European Sleep Research Society, Sleep Europe 2022 in Athens, Greece, on 29 September 2022: Beyond the state of the art in diagnosis of OSA – results from Sleep Revolution and “Beyond the PAP” – Personalized treatment of obstructive sleep apnoea in the Sleep Revolution project.

Beyond the state of the art in diagnosis of OSA – results from Sleep Revolution

Session: Beyond the state of the art in diagnosis of OSA – results from Sleep Revolution
Topic: Three-night sleep study for the diagnosis of OSA – pros and cons
Presenter: Dimitri Ferretti, Iceland

Session: Beyond the state of the art in diagnosis of OSA – results from Sleep Revolution
Topic: Sleep Revolution app – adherence to an electronic sleep diary and cognitive tests
Presenter: Anna Sigríður Islind, Iceland

Session: Beyond the state of the art in diagnosis of OSA – results from Sleep Revolution
Topic: Association of desaturation event characteristics to daytime symptoms and comorbidities
Presenter: Dr. Timo Leppänen, Finland

Session: Beyond the state of the art in diagnosis of OSA – results from Sleep Revolution
Topic: Deep learning-based simultaneous segmentation of sleep stages and breathing obstructions for automatic diagnosis of OSA
Presenter: Riku Huttunen, Finland

"Beyond the PAP" - Personalized treatment of obstructive sleep apnoea in the Sleep Revolution project

Session: “Beyond the PAP” – Personalized treatment of obstructive sleep apnoea in the Sleep Revolution project
Topic: Personalized therapeutical concepts: basic concepts and translation to clinics
Presenter: Prof. Winfried J. Randerath, Germany

Session: “Beyond the PAP” – Personalized treatment of obstructive sleep apnoea in the Sleep Revolution project
Topic: Orofacial myofunctional training with autofeedback (OMTa)
Presenter: Dr. PhD Harald Hrubos-Strøm, Norway

Session: “Beyond the PAP” – Personalized treatment of obstructive sleep apnoea in the Sleep Revolution project
Topic: Pharmacological therapies
Presenter: Prof. Jan A. Hedner, Sweden

Session: “Beyond the PAP” – Personalized treatment of obstructive sleep apnoea in the Sleep Revolution project
Topic: Lifestyle intervention: What is enough dose of exercise?
Presenter: Prof. Dr. Jose Miguel Saavedra, Iceland

Current Participants

gabriel-pires-hd

Gabriel Natan
Pires

RESEARCH METHODOLOGIST

Jennifer
Thomsen

ADMINISTRATIVE LEAD

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