Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis
Abstract
Effective sleep analysis is hampered by the lack of automated tools catering to disordered sleep patterns and cumbersome monitoring hardware. In this paper, we apply deep learning on a set of 57 EEG features extracted from a maximum of two EEG channels to accurately differentiate between patients with insomnia or controls with no sleep complaints. We investigated two different approaches to achieve this. The first approach used EEG data from the whole sleep recording irrespective of the sleep stage (stage-independent classification), while the second used only EEG data from insomnia-impacted specific sleep stages (stage-dependent classification). We trained and tested our system using both healthy and disordered sleep collected from 41 controls and 42 primary insomnia patients. When compared with manual assessments, an NREM + REM based classifier had an overall discrimination accuracy of 92% and 86% between two groups using both two and one EEG channels, respectively. These results demonstrate that deep learning can be used to assist in the diagnosis of sleep disorders such as insomnia.
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AI can aid clinicians in making sleep disorder diagnoses.
“First, it can aid clinicians in making sleep disorder diagnoses94.”
From The future of sleep health: a data-driven revolution in sleep science and medicine (2020) · cites this paper as [94]
Cites
Baglioni, C., Battagliese, G., Feige, B., Spiegelhalder, K., Nissen, C., Voderholzer, U., Lombardo, C., & Riemann, D. (2011) · Journal of Affective Disorders · doi:10.1016/j.jad.2011.01.011 · Cited by 2,779 · see citing works
The hyperarousal model of insomnia: A review of the concept and its evidence
Riemann, D., Spiegelhalder, K., Feige, B., Voderholzer, U., Berger, M., Perlis, M., & Nissen, C. (2010) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2009.04.002 · Cited by 1,789 · cited 2× · see citing works
“To provide improved resolution, as insomnia has been seen to increase EEG spectral band power [21], [22], we computed absolute power in ten sub-bands, δ1 (0.”
Buxton, O. M., & Marcelli, E. (2010) · Social Science & Medicine · doi:10.1016/j.socscimed.2010.05.041 · Cited by 818 · see citing works
Sleep changes in the disorder of insomnia: a meta-analysis of polysomnographic studies
Baglioni, C., Regen, W., Teghen, A., Spiegelhalder, K., Feige, B., Nissen, C., & Riemann, D. (2014) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2013.04.001 · Cited by 386 · see citing works
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Deep learning for electroencephalogram (EEG) classification tasks: a review
Craik, A., He, Y., & Contreras-Vidal, J. L. (2019) · Journal of Neural Engineering · doi:10.1088/1741-2552/ab0ab5 · Cited by 1,758 · see citing works
The future of sleep health: a data-driven revolution in sleep science and medicine
Perez-Pozuelo, I., Zhai, B., Palotti, J., Mall, R., Aupetit, M., Garcia-Gomez, J. M., Taheri, S., Guan, Y., & Fernandez-Luque, L. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0244-4 · Cited by 311 · see citing works
A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers
Zhang, X., Yao, L., Wang, X., Monaghan, J., McAlpine, D., & Zhang, Y. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc902 · Cited by 296 · see citing works
Clinical applications of artificial intelligence in sleep medicine: A sleep clinician’s perspective
Bandyopadhyay, A., & Goldstein, C. (2023) · Sleep And Breathing · doi:10.1007/s11325-022-02592-4 · Cited by 108 · see citing works
Deep learning applied to electroencephalogram data in mental disorders: a systematic review
de Bardeci, M., Ip, C. T., & Olbrich, S. (2021) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108117 · Cited by 71 · see citing works
- Authors
- Shahin, M., Ahmed, B., Hamida, S. T.-B., Mulaffer, F. L., Glos, M., & Penzel, T.
- Affiliations
- Texas A&M University at Qatar (QA)
- Charité – Universitätsmedizin Berlin (DE)
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2017-11
- Volume
- 21
- Issue
- 6
- Pages
- 1546-1553
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2568045458
- Cited by
- 96
- References
- 4
- Citations by year
- 2026: 4 · 2025: 11 · 2024: 15 · 2023: 13 · 2022: 9 · 2021: 19 · 2020: 13 · 2019: 7 · 2018: 3 · 2017: 2
- FWCI
- 3.93
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Biomedical and Health Informatics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- NPRP (5-1327-2-568)
- Qatar National Research Fund
- Qatar Foundation
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- insomnia, sleep disorders, EEG features, deep learning classification, automated diagnosis, sleep stages, clinical decision support
- MeSH terms
- Machine Learning, Adult, Diagnosis, Computer-Assisted, Electroencephalography, Female, Humans, Sleep Initiation and Maintenance Disorders, Male, Middle Aged, Signal Processing, Computer-Assisted, Sleep Stages, Polysomnography
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