Artificial Intelligence in Sleep Medicine: The Dawn of a New Era

Abstract

Artificial intelligence (AI) and machine learning (ML) are set to revolutionize the field of sleep medicine. The inherently digital nature of sleep data, such as polysomnography signals, makes it well-suited for AI analysis. AI enables automation of sleep study scoring, potentially reducing time and cost while improving consistency compared to manual scoring. Beyond autoscoring, AI can enhance diagnosis and personalized treatment of sleep disorders like obstructive sleep apnea by identifying complex patterns and endotypes. ML models can predict CPAP therapy adherence and outcomes, enabling tailored interventions. AI also advances sleep neuroscience research by extracting novel EEG features that may serve as early markers of neurodegenerative disorders. The integration of AI with big data opens exciting opportunities for impactful real-world research, although challenges remain in data standardization and quality. As AI becomes integral to sleep medicine, it is crucial to address ethical considerations such as potential bias and the need for transparency and validation of AI tools. Ongoing research, collaboration, and rigorous evaluation are essential to harness the transformative potential of AI in sleep medicine while ensuring equitable implementation across diverse populations.

Full text

EDITORIAL
Articial Intelligence in Sleep Medicine: The Dawn
of a New Era
Ahmed Salem BaHammam
1,2
1
Department of Medicine, University Sleep Disorders Center and Pulmonary Service, King Saud University, Riyadh, Saudi Arabia;
2
King Saud
University Medical City, Riyadh, Saudi Arabia
Correspondence: Ahmed Salem BaHammam, University Sleep Disorders Center, Department of Medicine, College of Medicine, King Saud University,
Box 225503, Riyadh, 11324, Saudi Arabia, Tel +966-11-467-9495, Fax +966-11-467-9179, Email ashammam2@gmail.com
The convergence of articial intelligence (AI) and machine learning (ML) with modern medicine has not only opened up
unprecedented opportunities for innovation but also gained recognition from prominent publications, including Nature
and Science of Sleep, for their transformative potential in sleep medicine research and clinical practice. As such, Nature
and Science of Sleep welcomes explorations into the applications and implications of AI in the eld.
AI enables machines and software to perform tasks such as problem-solving, pattern recognition, learning, and
understanding language, which traditionally required human cognition. Meanwhile, ML—a subset of AI—enhances these
capabilities further with algorithms that learn and improve from data over time. The eld of sleep medicine stands at the
threshold of a transformative revolution, propelled by swift progress in AI and ML. Due to the inherently digital nature of
data collected in this eld, sleep medicine is uniquely positioned to harness AI and ML. Polysomnography, the gold
standard diagnostic tool, produces extensive physiological data in digital formats such as EEG, ECG, EMG, and
respiratory signals, making it ripe for AI analysis. This wealth of structured digital data makes sleep medicine an
ideal candidate for the application of AI and ML algorithms, which excel at identifying complex patterns and relation-
ships within large datasets. Additionally, the widespread availability of consumer sleep technologies, like wearables,
expands these opportunities, allowing ML to extract novel insights from extensive real-world sleep data.
1,2
This editorial
aims to highlight the current uses of AI in sleep medicine and research, validation and ethical challenges, and the exciting
future prospects. Figure 1 illustrates the breadth of potential AI applications in sleep medicine practice and research.
Automating Sleep Study Scoring
The application of AI to automate sleep study scoring was one of the earliest and most promising use cases in sleep
medicine, primarily due to the inherently digital nature of collected data.
3
Manual scoring of sleep studies is both time-
consuming and labor-intensive, and liable to inter-scorer variability. By contrast, ML algorithms trained on large datasets
have demonstrated sleep staging accuracy comparable to interrater reliability among human scorers, with reported
Cohen’s kappa (κ) values reaching up to 0.80.
3
Such high-performing algorithms can streamline the sleep staging
process, potentially reducing the time and cost associated with manual scoring. Moreover, standardizing sleep staging
through autoscoring can enhance the consistency and reliability of research ndings across various studies and institu-
tions. AI does not have to necessarily replace human scoring, but it can help in saving time. A recent study showed that
semi-automated sleep scoring systems signicantly reduce the workload on clinicians by directing their attention to the
most critical areas, thereby enhancing overall efciency.
4
This advancement highlights the potential of AI in standardiz-
ing sleep scoring and reducing interrater variability—an important development as it provides independent verication of
the real-world accuracy of these tools. Nevertheless, thoroughly validating AI models using diverse clinical data as a vital
step for their adoption in clinical settings is still needed.
5
Building on these advancements, the FDA has cleared a few auto-scoring software systems for use,
6
and the
American Academy of Sleep Medicine (AASM) has initiated a two-year pilot program to certify autoscoring software,
Nature and Science of Sleep 2024:16 445–450 445
© 2024 BaHammam. This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.
php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/). By accessing the
work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For
permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
Nature and Science of Sleep Dovepress
open access to scientific and medical research
Open Access Full Text Article
Received: 18 April 2024
Accepted: 25 April 2024
Published: 30 April 2024

aiming to further validate these tools against expert manual scoring.
7
This certication program aims to independently
assess the real-world accuracy of autoscoring software by comparing its performance to manual scoring by sleep experts.
By doing so, it provides accredited sleep facilities with condence in the reliability of autoscored results.
Enhancing Diagnosis and Personalized Treatment
Beyond autoscoring, AI can potentially revolutionize the eld of sleep medicine by enabling early detection, personalized
treatment, and improved management of sleep disorders. AI is being utilized to improve the diagnosis of sleep disorders such
as obstructive sleep apnea (OSA), going beyond just the apnea-hypopnea index.
8
Traditionally, OSA is diagnosed using
polysomnography, which requires an overnight stay in a sleep lab or home sleep apnea testing. Now, researchers are
developing AI models that can detect OSA using more accessible and affordable methods. For example, smartwatches can
now monitor blood oxygen levels (SpO2) with high accuracy, offering a potential tool for early OSA detection.
9–11
Moreover,
AI-based predictive models have shown promising accuracy in identifying individuals at risk of developing OSA using simple
predictors like age, sex, and body mass index, with reasonable success.
12,13
Additionally, AI has demonstrated promise in
aiding the diagnosis and subtyping of narcolepsy by analyzing clinical and polysomnographic data.
14,15
The application of AI and ML in personalizing treatments for sleep disorders offers a promising method for
enhancing patient adherence and outcomes.
16
AI algorithms have the capability to analyze extensive datasets to identify
trends and relationships among patient traits, sleep physiology, and responses to treatment. This detailed analysis
facilitates precise phenotyping and endotyping of OSA, paving the way for the creation of customized treatment
strategies that focus on the unique aspects of the disorder.
ML models are capable of predicting essential characteristics linked to OSA, including upper airway collapsibility,
reduced muscle responsiveness, a low arousal threshold, and abnormal ventilatory control.
17,18
A recent study introduced
a new data-driven approach, employing unsupervised multivariate principal component analyses and supervised machine
learning, to explore how four key OSA endotypes affect the severity of OSA as dened by polysomnography.
19
The
study also aimed to understand how typical polysomnographic and clinical variables might predict these endotypes.
19
Understanding a patient’s specic endotype can directly tailor treatment strategies. For instance, a machine learning
Figure 1 The gure showcases the spectrum of potential AI applications in sleep medicine practice and research, from advancing diagnostic accuracy and automating sleep
study analyses to informing research through data-driven predictions and optimizing therapeutic strategies.
https://doi.org/10.2147/NSS.S474510
DovePress
Nature and Science of Sleep 2024:16
446
BaHammam Dovepress
Powered by TCPDF (www.tcpdf.org)

model was employed to forecast the results of different treatment methods, such as oral appliances,
20
allowing patients to
be matched with the therapy that would likely be most effective for them.
ML algorithms have demonstrated high accuracy in predicting CPAP therapy adherence in OSA patients.
21,22
By
analyzing large datasets, AI can identify correlations between patient characteristics and treatment responses, enabling
clinicians to tailor interventions to individual needs. This personalized approach to sleep therapy could revolutionize
patient care, improving outcomes and quality of life.
Randomized controlled trials have historically expressed skepticism regarding the efcacy of CPAP therapy in
reducing cardiovascular events among patients with OSA, often yielding debated and disappointing results.
23
However, recent advancements and the utility of ML have introduced new hope for discerning the efcacy of CPAP
by recognizing heterogeneity within the OSA population. A recent study utilized ML in the ISAACC trial dataset to
identify subgroups exhibiting varied responses to CPAP therapy.
24
The investigators found that CPAP therapy was
associated with fewer cardiovascular events in patients who have shorter durations of apnea events in OSA, compared to
those with longer durations who experienced higher risks.
24
This study underscores the importance of additional research
utilizing ML to create customized treatment approaches tailored to individual patient characteristics. Consequently, ML
could potentially redene the approach to precision medicine in OSA management, highlighting the importance of
incorporating personalized treatment protocols in future trials to overturn previously negative outcomes.
16
Similar
approaches can be investigated in other sleep disorders.
Furthermore, the application of AI/ML methods could signicantly enhance the prognostic assessment of OSA,
thereby improving patient care and outcomes. For instance, by employing advanced ML techniques to analyze the full
ventilatory distribution histogram in conjunction with other critical measures of OSA severity—such as hypoxic burden
and arousal indices—these sophisticated algorithms can offer a more comprehensive and precise evaluation of the disease
outcome.
25,26
Such detailed assessments are crucial for understanding the complete effects of OSA on patients and
tailoring treatment to individual needs more effectively.
In the eld of chronotherapy, mathematical modeling has shown promise in optimizing the timing of chemotherapy
treatments based on the circadian rhythms of patients to improve efcacy and minimize toxicity. These models streamline
the interpretation of complex drug interactions and timings, making it far more feasible to align treatments with circadian
cycles for improved survival outcomes. For instance, studies applying mathematical modeling in metastatic colorectal
cancer have found that treatments timed according to patient-specic circadian rhythms signicantly improved survival
compared to traditional schedules.
27
Recently, an ML method was developed to predict circadian time accurately using
just a few blood samples.
28
This innovation facilitates personalized chronotherapy in clinical settings by accurately
determining each patient’s internal physiological clock state. Such advancements highlight the potential of AI to tailor
treatment schedules, thereby enhancing the efcacy and safety of therapies.
29
AI and Advancing Sleep Medicine Research with Big Data
Integrating AI and ML with big data presents exciting opportunities for advancing sleep medicine research and
improving patient care.
30
Due to the wealth of digital physiological data collected through sleep studies, wearable
devices, and self-quantication systems, data analysis in sleep medicine research is particularly well-suited for AI/ML
applications.
31
The selection of appropriate training data is vital for developing accurate and generalizable AI models in
sleep medicine research, as the size, quality, and representativeness of the dataset can signicantly affect model
performance and the likelihood of overtting or undertting, which are issues related to how well the model predicts
new, unseen data compared to how it performs on the training data.
5
To mitigate these risks, researchers should strive to
use large, diverse, and representative datasets for training AI models, employ random sampling methods to guarantee that
the data accurately reects the target population, and apply iterative model training along with independent validation to
evaluate the stability and generalizability of the models developed.
32
Initiatives such as the National Sleep Research Resource (NSRR) and the UK Biobank have made large sleep datasets
publicly available, enabling researchers worldwide to leverage this valuable data for analysis. The proposed Human
Sleep Project aims to gather real-world sleep data from millions of people using online questionnaires, sleep diaries, and
recording devices to establish the true role of sleep and answer fundamental questions about sleep duration, quality, and
Nature and Science of Sleep 2024:16 https://doi.org/10.2147/NSS.S474510
DovePress
447
Dovepress BaHammam
Powered by TCPDF (www.tcpdf.org)

the impact of genetic and environmental factors.
33
These publicly available resources open promising new avenues for
impactful real-world research on sleep medicine using AI. However, challenges remain regarding data standardization,
privacy issues, and the integration of multi-omics data (eg, transcriptomics, proteomics, metabolomics). Furthermore,
ensuring optimal data quality and having independent validation datasets are necessary, given that most existing
polysomnogram datasets originate from research studies with specic inclusion criteria and may not be applicable to
real clinical practice.
34
Addressing these challenges will be necessary for realizing the complete capacity of large datasets
and AI/ML in sleep medicine research and clinical applications, clearing the path for personalized sleep medicine and
improved patient outcomes.
16
AI in Sleep Neuroscience
In sleep neuroscience, AI has greatly improved the analysis of EEG signals, taking its use beyond just routine clinical
applications. Machine learning classiers of EEG signals during sleep do more than diagnose sleep-wake disorders and
categorize different sleep stages. They also provide a valuable method for studying the functions of sleep. Additionally, ML
models that analyze baseline EEG features have demonstrated potential in predicting various neurological diseases
associated with sleep disorders. Specically, they have shown effectiveness in determining the timing and subtype of
phenoconversion in patients with idiopathic Rapid Eye Movement sleep behavior disorder, a condition frequently preceding
neurodegenerative diseases.
35,36
Similarly, an ML algorithm analyzing sleep structure, frequency band powers, spindles,
slow oscillations, and coherence in 10,784 sleep studies across 8044 participants effectively distinguished between those
with dementia, mild cognitive impairment, and no cognitive impairments.
37
This ability underscores the signicant
potential of AI in detecting early markers of neurodegenerative disorders linked to sleep abnormalities, underscoring the
need for more comprehensive international research to rene these predictive models.
Ethical Considerations and Recommendations
As we explore the new frontier of AI applications in sleep medicine, addressing ethical and legal considerations is
essential. The AASM, in their position statement, has raised concerns about AI potentially exacerbating existing
healthcare inequities, such as sex disparities in evaluating sleep-disordered breathing.
3
It is vital to train AI algorithms
on diverse datasets to prevent bias and promote equitable care. While AI tools can enhance clinical decision-making and
data analysis, the ultimate responsibility for patient diagnosis, treatment, and research integrity rests with sleep medicine
providers and researchers. The AASM also offers several recommendations for sleep disorder centers considering AI-
based tools. These include demanding transparency from manufacturers about the intended population, goals, and
characteristics of datasets used for developing AI programs. Furthermore, AI applications should be subjected to
thorough testing on independent, standardized datasets representative of the target patient population to ensure general-
izability and performance on par with expert evaluations. Lastly, manufacturers should support sleep centers in assessing
the real-world efcacy and utility of AI software within their specic settings, ensuring that physician review and options
for manual rescoring of sleep studies are available to guarantee accurate diagnoses and optimal patient care. Moreover, it
is evident that we must validate and standardize ML methodologies in research, particularly in healthcare applications
like sleep medicine.
16
Robust data handling, model development, validation, and reporting guidelines should be
established to warrant the reliability, fairness, and reproducibility of ML-based ndings, facilitating their responsible
translation into clinical practice for personalized sleep disorders diagnosis and management.
38
In summary, the dawn of AI and ML in sleep medicine marks a pivotal era of enhanced diagnostic precision,
personalized treatment, and rapid advancements in quality research. This transition, driven by extensive research and big
data analytics that identify different endotypes of sleep disorders, opens new avenues for groundbreaking research while
necessitating rigorous ethical oversight to ensure its equitable implementation across diverse populations. As AI
technologies become increasingly integral to sleep medicine, ongoing research, interdisciplinary collaboration, and
thorough evaluation are essential to maximize their transformative potential globally.
https://doi.org/10.2147/NSS.S474510
DovePress
Nature and Science of Sleep 2024:16
448
BaHammam Dovepress
Powered by TCPDF (www.tcpdf.org)

Disclosure
Professor BaHammam is the Editor-in-Chief for Nature and Science of Sleep. The author reports no other conicts of
interest in this work.
References
1. Khosla S, Deak MC, Gault D, et al. Consumer sleep technology: an American academy of sleep medicine position statement. J Clin Sleep Med.
2018;14(5):877–880. doi:10.5664/jcsm.7128
2. BaHammam AS, Pandi-Perumal SR, Hunasikatti M. Wearable Technologies/Consumer Sleep Technologies in Relation to Sleep Disorders
Developments in the Last Decade. In: BaHammam AS, Hunasikatti M, editors. Sleep Apnea Frontiers: Pathophysiology, Diagnosis, and
Treatment Strategies. Singapore: Springer Nature Singapore; 2023:145–160.
3. Goldstein CA, Berry RB, Kent DT, et al. Articial intelligence in sleep medicine: an American academy of sleep medicine position statement.
J Clin Sleep Med. 2020;16(4):605–607. doi:10.5664/jcsm.8288
4. Bechny M, Monachino G, Fiorillo L, et al. Bridging AI and clinical practice: integrating automated sleep scoring algorithm with uncertainty-guided
physician review. Nat Sci Sleep. 2024;2024:1.
5. Alattar M, Govind A, Mainali S. Articial intelligence models for the automation of standard diagnostics in sleep medicine—a systematic review.
Bioengineering. 2024;11(3):206. doi:10.3390/bioengineering11030206
6. Choo BP, Mok Y, Oh HC, et al. Benchmarking performance of an automatic polysomnography scoring system in a population with suspected sleep
disorders. Front Neurol. 2023;14:1123935. doi:10.3389/fneur.2023.1123935
7. American Academy of Sleep Medicine. American academy of sleep medicine. AASM autoscoring certication: sleep stage pilot program. Darien,
IL: American Academy of Sleep Medicine; 2023. Available from: https://aasm.org/about/industry-programs/autoscoring-certication/#:~:text=The
%20AASM%20is%20excited%20to,the%20performance%20of%20autoscoring%20systems. Accessed April 15, 2024.
8. Ferreira-Santos D, Amorim P, Silva Martins T, Monteiro-Soares M, Pereira Rodrigues P. Enabling early obstructive sleep apnea diagnosis with
machine learning: systematic review. J Med Internet Res. 2022;24(9):e39452. doi:10.2196/39452
9. Serrano Alarcón Á, Martínez madrid N, Seepold R. A minimum set of physiological parameters to diagnose obstructive sleep apnea syndrome
using non-invasive portable monitors. a systematic review. Life. 2021;11(11). doi:10.3390/life11111249
10. Zhao R, Xue J, Zhang X, et al. Comparison of ring pulse oximetry using reective photoplethysmography and PSG in the Detection of OSA in
Chinese adults: a pilot study. Nat Sci Sleep. 2022;14:1427–1436. doi:10.2147/NSS.S367400
11. Almarshad MA, Al-Ahmadi S, Islam MS, BaHammam AS, Soudani A. Adoption of transformer neural network to improve the diagnostic
performance of oximetry for obstructive sleep apnea. Sensors. 2023;23(18):7924. doi:10.3390/s23187924
12. Yang H, Lu S, Yang L. Clinical prediction models for the early diagnosis of obstructive sleep apnea in stroke patients: a systematic review. Syst Rev.
2024;13(1):38. doi:10.1186/s13643-024-02449-9
13. Kuan YC, Hong CT, Chen PC, Liu WT, Chung CC. Logistic regression and articial neural network-based simple predicting models for obstructive
sleep apnea by age, sex, and body mass index. Math Biosci Eng. 2022;19(11):11409–11421. doi:10.3934/mbe.2022532
14. Shen N, Luo T, Chen C, et al. Towards an automatic narcolepsy detection on ambiguous sleep staging and sleep transition dynamics joint model.
J Neural Eng. 2022;19(5):056009. doi:10.1088/1741-2552/ac8c6b
15. Cesari M, Egger K, Stefani A, et al. Differentiation of central disorders of hypersomnolence with manual and articial-intelligence-derived
polysomnographic measures. Sleep. 2023;46(2). doi:10.1093/sleep/zsac288
16. BaHammam A. Personalizing obstructive sleep apnea therapy using machine learning: insights from the ISAACC trial. Ann Am Thorac Soc. 2024.
doi:10.1513/AnnalsATS.202403-308ED
17. Nemati S, Orr J, Malhotra A. Data-driven phenotyping: graphical models for model-based phenotyping of sleep apnea. IEEE Pulse. 2014;5
(5):45–48. doi:10.1109/MPUL.2014.2339402
18. Brennan HL, Kirby SD. The role of articial intelligence in the treatment of obstructive sleep apnea. J Otolaryngol. 2023;52(1):7. doi:10.1186/
s40463-023-00621-0
19. Dutta R, Delaney G, Toson B, et al. A novel model to estimate key obstructive sleep apnea endotypes from standard polysomnography and clinical
data and their contribution to obstructive sleep apnea severity. Ann Am Thorac Soc. 2021;18(4):656–667. doi:10.1513/AnnalsATS.202001-064OC
20. Dutta R, Tong BK, Eckert DJ. Development of a physiological-based model that uses standard polysomnography and clinical data to predict oral
appliance treatment outcomes in obstructive sleep apnea. J Clin Sleep Med. 2022;18(3):861–870. doi:10.5664/jcsm.9742
21. Eguchi K, Yabuuchi T, Nambu M, et al. Investigation on factors related to poor CPAP adherence using machine learning: a pilot study. Sci Rep.
2022;12(1):19563. doi:10.1038/s41598-022-21932-8
22. Scioscia G, Tondo P, Foschino Barbaro MP, et al. Machine learning-based prediction of adherence to continuous positive airway pressure (CPAP) in
obstructive sleep apnea (OSA). Inform Health Soc Care. 2022;47(3):274–282. doi:10.1080/17538157.2021.1990300
23. Qasrawi SO, BaHammam AS. Role of Sleep and Sleep Disorders in Cardiometabolic Risk: a Review and Update. Current Sleep Med Rep. 2024;10
(1):34–50. doi:10.1007/s40675-024-00276-x
24. Cohen O, Sánchez-de-la-Torre M, Al-Taie Z, et al. Heterogeneous effects of CPAP in Non-Sleepy OSA on CVD outcomes: post-hoc machine
learning analysis of the ISAACC Trial (ECSACT Study). Ann Am Thorac Soc. 2024;21:1.
25. Parekh A, Kam K, Wickramaratne S, et al. Ventilatory burden as a measure of obstructive sleep apnea severity is predictive of cardiovascular and
all-cause mortality. Am J Respir Crit Care Med. 2023;208(11):1216–1226. doi:10.1164/rccm.202301-0109OC
26. Wickramaratne S, Kam K, Tolbert TM, et al. Combination of ventilatory, hypoxic, and arousal burden predicts short- and long-term consequences
of OSA better than the apnea-hypopnea index [abstract]. Am J Respir Crit Care Med. 2023;207:A5968.
27. Hesse J, Martinelli J, Aboumanify O, Ballesta A, Relogio A. A mathematical model of the circadian clock and drug pharmacology to optimize
irinotecan administration timing in colorectal cancer. Comput Struct Biotechnol J. 2021;19:5170–5183. doi:10.1016/j.csbj.2021.08.051
28. Braun R, Kath WL, Iwanaszko M, et al. Universal method for robust detection of circadian state from gene expression. Proc Natl Acad Sci U S A.
2018;115(39):E9247–e56. doi:10.1073/pnas.1800314115
Nature and Science of Sleep 2024:16 https://doi.org/10.2147/NSS.S474510
DovePress
449
Dovepress BaHammam
Powered by TCPDF (www.tcpdf.org)

29. Zaki NFW, Yousif M, BaHammam AS, et al. Chronotherapeutics: recognizing the importance of timing factors in the treatment of disease and sleep
disorders. Clin Neuropharmacol. 2019;42(3):80–87. doi:10.1097/WNF.0000000000000341
30. Leppanen T, Varon C, de Zambotti M, Myllymaa S. Machine learning and wearable technology in sleep medicine. Front Digit Health.
2022;4:845879. doi:10.3389/fdgth.2022.845879
31. Göktepe-Kavis P, Aellen FM, Alnes SL, Tzovara A. Sleep research in the era of AI. Clin Transl Neurosci. 2024;8(1):13. doi:10.3390/ctn8010013
32. Millard K, Richardson M. On the importance of training data sample selection in random forest image classication: a case study in peatland
ecosystem mapping. Remote Sensing. 2015;7(7):8489–8515. doi:10.3390/rs70708489
33. Roenneberg T. The human sleep project. Nature. 2013;498:427–8. doi:10.1038/498427a
34. Bandyopadhyay A, Goldstein C. Clinical applications of articial intelligence in sleep medicine: a sleep clinician’s perspective. Sleep Breath.
2023;27(1):39–55. doi:10.1007/s11325-022-02592-4
35. Jeong E, Shin YW, Byun JI, et al. EEG-based machine learning models for the prediction of phenoconversion time and subtype in iRBD. Sleep.
2024. doi:10.1093/sleep/zsae031
36. Cesari M, Christensen JAE, Muntean ML, et al. A data-driven system to identify REM sleep behavior disorder and to predict its progression from
the prodromal stage in Parkinson’s disease. Sleep Med. 2021;77:238–248. doi:10.1016/j.sleep.2020.04.010
37. Adra N, Sun H, Ganglberger W, et al. Optimal spindle detection parameters for predicting cognitive performance. Sleep. 2022;45(4). doi:10.1093/
sleep/zsac001
38. Klement W, El Emam K. Consolidated reporting guidelines for prognostic and diagnostic machine learning modeling studies: development and
validation. J Med Internet Res. 2023;25:e48763.
Nature and Science of Sleep Dovepress
Publish your work in this journal
Nature and Science of Sleep is an international, peer-reviewed, open access journal covering all aspects of sleep science and sleep medicine,
including the neurophysiology and functions of sleep, the genetics of sleep, sleep and society, biological rhythms, dreaming, sleep disorders
and therapy, and strategies to optimize healthy sleep. The manuscript management system is completely online and includes a very quick and fair
peer-review system, which is all easy to use. Visit http://www.dovepress.com/testimonials.php to read real quotes from published authors.
Submit your manuscript here: https://www.dovepress.com/nature-and-science-of-sleep-journal
DovePress Nature and Science of Sleep 2024:16
450
BaHammam Dovepress
Powered by TCPDF (www.tcpdf.org)