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Brain Sciences
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Artificial Intelligence in Sleep Medicine
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Artificial Intelligence in Sleep Medicine

A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neurotechnology and Neuroimaging".

Deadline for manuscript submissions: closed (15 January 2025) | Viewed by 2998

Special Issue Editors

Dr. Ambra Stefani

E-Mail Website
Guest Editor
Sleep Disorders Clinic, Department of Neurology, Medical University of Innsbruck, Anichstrasse 35, 6020 Innsbruck, Austria
Interests: sleep; video-polysomnography; REM sleep behavior disorder; RBD; restless legs syndrome; RSL; neurodegeneration; REM sleep without atonia; RWA; neuroimaging
Dr. Wolfgang Ganglberger

E-Mail Website
Guest Editor
Clinical Data Animation Center, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, 330 Brookline Avenue, Boston, MA 02215, USA
Interests: sleep; polysomnography; quantified electroencephalography; machine learning; artificial intelligence; multivariate analysis; wearables; personalized medicine; neurodegeneration; mood disorders

Special Issue Information

Dear Colleagues,

Background & history of this topic:

Artificial intelligence (AI) entered sleep medicine just a few years ago mainly as a research tool. It has since evolved to aid in clinical evaluation and become an important addition to the tools available for detecting and assessing sleep disorders. Today, AI is an integral part of sleep medicine and will likely play an even bigger role in the future as wearable and home-based sleep trackers become more prevalent.

Aim and scope of the Special Issue:

This Special Issue aims to present cutting-edge research on AI applications in sleep medicine, covering normal sleep patterns and the full spectrum of sleep disorders. We welcome original research as well as review articles on topics including narcolepsy/hypersomnias, parasomnias, restless legs syndrome, sleep apnea, and circadian rhythm disorders. Studies exploring the links between sleep and health are of interest, particularly cardiovascular diseases and brain health/neurodegeneration. We also invite opinion pieces on how AI is changing the sleep medicine field.

Cutting-edge research:

  • AI-assisted diagnosis of sleep disorders based on clinical questionnaires, wearable data, nearables, imaging, and other digital biomarkers;
  • Neural network models for optimizing treatment of sleep apnea, insomnia, and other sleep disorders;
  • Natural language processing for analyzing patient sleep logs and clinical notes;
  • Predictive analytics to identify individuals at risk for sleep disturbances using demographics, medical history, and lifestyle data;
  • Algorithms for automated scoring and classification of sleep stages and events using polysomnography, actigraphy, and other physiological data;
  • Novel sensors and wearable devices enhanced by machine learning for sleep monitoring and phenotype discovery;
  • AI for discovering connections between sleep and other health conditions such as Alzheimer's, depression, obesity, etc.;
  • Improving adherence and efficacy of digital CBT for insomnia using reinforcement learning and virtual agents;
  • Ethical implications of using AI in sleep medicine clinical practice and research.

What kinds of papers we are soliciting:

  • Original research articles demonstrating novel applications of AI in sleep medicine;
  • Review articles synthesizing major themes, techniques, challenges, and opportunities in the field;
  • Perspective pieces on the impacts of AI on the sleep medicine clinician workflow, training, etc.;
  • Commentaries discussing limitations, best practices, and future outlook for AI in sleep research and care;
  • Viewpoint articles highlighting implications for health policy, regulation, and access to care;
  • Technical reports detailing new AI methodologies tailored to sleep data and disorders;
  • Pilot studies examining feasibility of implementing AI tools in clinical settings;
  • Meta-analyses and systematic reviews of AI accuracy for sleep assessment across multiple studies.

Dr. Ambra Stefani
Dr. Wolfgang Ganglberger
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Brain Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sleep
  • artificial intelligence
  • machine-learning
  • brain health
  • sleep scoring
  • sleep disorders
  • digital health
  • wearables
  • digital biomarkers

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Published Papers (2 papers)

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Research

21 pages, 6714 KiB  
Article
Single-Channel Sleep EEG Classification Method Based on LSTM and Hidden Markov Model
by Wan Chen, Yanping Cai, Aihua Li, Yanzhao Su and Ke Jiang
Brain Sci. 2024, 14(11), 1087; https://doi.org/10.3390/brainsci14111087 - 29 Oct 2024
Viewed by 814
Abstract
Background: The single-channel sleep EEG has the advantages of convenient collection, high-cost performance, and easy daily use, and it has been widely used in the classification of sleep stages. Methods: This paper proposes a single-channel sleep EEG classification method based on long short-term [...] Read more.
Background: The single-channel sleep EEG has the advantages of convenient collection, high-cost performance, and easy daily use, and it has been widely used in the classification of sleep stages. Methods: This paper proposes a single-channel sleep EEG classification method based on long short-term memory and a hidden Markov model (LSTM-HMM). First, the single-channel EEG is decomposed using wavelet transform (WT), and multi-domain features are extracted from the component signals to characterize the EEG characteristics fully. Considering the temporal nature of sleep stage changes, this paper uses a multi-step time series as the input for the model. After that, the multi-step time series features are input into the LSTM. Finally, the HMM improves the classification results, and the final prediction results are obtained. Results: A complete experiment was conducted on the Sleep-EDFx dataset. The results show that the proposed method can extract deep information from EEG and make full use of the sleep stage transition rule. The proposed method shows the best performance in single-channel sleep EEG classification; the accuracy, macro average F1 score, and kappa are 82.71%, 0.75, and 0.76, respectively. Conclusions: The proposed method can realize single-channel sleep EEG classification and provide a reference for other EEG classifications. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sleep Medicine)
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15 pages, 1011 KiB  
Article
Machine Learning Predicts Phenoconversion from Polysomnography in Isolated REM Sleep Behavior Disorder
by Matteo Cesari, Andrea Portscher, Ambra Stefani, Raphael Angerbauer, Abubaker Ibrahim, Elisabeth Brandauer, Simon Feuerstein, Kristin Egger, Birgit Högl and Antonio Rodriguez-Sanchez
Brain Sci. 2024, 14(9), 871; https://doi.org/10.3390/brainsci14090871 - 28 Aug 2024
Viewed by 1271
Abstract
Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of alpha-synucleinopathies. This study aimed at developing a fully-automated machine learning framework for the prediction of phenoconversion in patients with iRBD by using data recorded during polysomnography (PSG). A total [...] Read more.
Isolated rapid eye movement (REM) sleep behavior disorder (iRBD) is a prodromal stage of alpha-synucleinopathies. This study aimed at developing a fully-automated machine learning framework for the prediction of phenoconversion in patients with iRBD by using data recorded during polysomnography (PSG). A total of 66 patients with iRBD were included, of whom 18 converted to an overt alpha-synucleinopathy within 2.7 ± 1.0 years. For each patient, a baseline PSG was available. Sleep stages were scored automatically, and time and frequency domain features were derived from electromyography (EMG) and electroencephalography (EEG) signals in REM and non-REM sleep. Random survival forest was employed to predict the time to phenoconversion, using a four-fold cross-validation scheme and by testing several combinations of features. The best test performances were obtained when considering EEG features in REM sleep only (Harrel’s C-index: 0.723 ± 0.113; Uno’s C-index: 0.741 ± 0.11; integrated Brier score: 0.174 ± 0.06). Features describing EEG slowing had high importance for the machine learning model. This is the first study employing machine learning applied to PSG to predict phenoconversion in patients with iRBD. If confirmed in larger cohorts, these findings might contribute to improving the design of clinical trials for neuroprotective treatments. Full article
(This article belongs to the Special Issue Artificial Intelligence in Sleep Medicine)
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