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Brain Sciences
Special Issues
Sleep Disorders and Neural Networks
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Sleep Disorders and Neural Networks

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

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 2929

Special Issue Editors

Prof. Dr. Rayleigh Ping Ying Chiang

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Guest Editor
1. Department of Otolaryngology Head & Neck Surgery, School of Medicine, China Medical University, Taichung, Taiwan
2. Department of Health Policy and Management, Bloomberg School of Public Health, Johns-Hopkins University, Baltimore, MD, USA
3. International Sleep Science and Technology Association (ISSTA), Taiwan Chapter, Taipei, Taiwan
4. Asia-Pacific Branch, Innovative Medical and Health Technology Center (IMHTC), Taipei, Taiwan
5. Sleep Well International Chain Sleep Centers, Taipei, Taiwan
Interests: sleep medicine; sleep disorders; sleep theranostics; sleep surgery; sleep technology; value-based medicine and health economics
Prof. Dr. Patrick J. Strollo

E-Mail Website
Co-Guest Editor
Professor of Medicine and Clinical and Translational Science, University of Pittsburg, UPMC, Pittsburgh, PA, USA
Interests: sleep medicine; sleep technology
Prof. Dr. Murat Özgören

E-Mail Website
Co-Guest Editor
Technology Transfer Office, Near East University, Nicosia, Cyprus
Interests: sleep technology; brain dynamics

Special Issue Information

Dear Colleagues,

Sleep is essential for people’s health and daily functioning. As sleep disorders become more prevalent in the general population, recent advances in artificial intelligence help us to alleviate stress caused by sleep disorders.

This Special Issue aims to investigate the emerging and evolving technologies that impact the practice of sleep medicine. We aim to look into topics such as the accuracy of sleep testing devices in diagnosing sleep disorders, the clinical and consumer use of sleep technology in the context of the pandemic, and the use of artificial intelligence (AI), machine learning (ML) and deep learning (DL) in processing multiple sensor and data sources to provide recognizable sleep data outputs.

This Special Issue welcomes original research, clinical studies and review articles that help advance our understanding of the application of artificial intelligence in sleep medicine. We hope this topic will bring together those who are working in sleep medicine and will be beneficial for both clinicians and scientists.

Prof. Dr. Rayleigh Ping Ying Chiang
Prof. Dr. Patrick J. Strollo
Prof. Dr. Murat Özgören
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 medicine
  • sleep technology
  • artificial intelligence
  • sleep disorders
  • obstructive sleep apnea

Published Papers (1 paper)

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9 pages, 862 KiB  
Brief Report
Discriminating Paradoxical and Psychophysiological Insomnia Based on Structural and Functional Brain Images: A Preliminary Machine Learning Study
by Mortaza Afshani, Ahmad Mahmoudi-Aznaveh, Khadijeh Noori, Masoumeh Rostampour, Mojtaba Zarei, Kai Spiegelhalder, Habibolah Khazaie and Masoud Tahmasian
Brain Sci. 2023, 13(4), 672; https://doi.org/10.3390/brainsci13040672 - 17 Apr 2023
Cited by 1 | Viewed by 1658
Abstract
Insomnia disorder (ID) is a prevalent mental illness. Several behavioral and neuroimaging studies suggested that ID is a heterogenous condition with various subtypes. However, neurobiological alterations in different subtypes of ID are poorly understood. We aimed to assess whether unimodal and multimodal whole-brain [...] Read more.
Insomnia disorder (ID) is a prevalent mental illness. Several behavioral and neuroimaging studies suggested that ID is a heterogenous condition with various subtypes. However, neurobiological alterations in different subtypes of ID are poorly understood. We aimed to assess whether unimodal and multimodal whole-brain neuroimaging measurements can discriminate two commonly described ID subtypes (i.e., paradoxical and psychophysiological insomnia) from each other and healthy subjects. We obtained T1-weighted images and resting-state fMRI from 34 patients with ID and 48 healthy controls. The outcome measures were grey matter volume, cortical thickness, amplitude of low-frequency fluctuation, degree centrality, and regional homogeneity. Subsequently, we applied support vector machines to classify subjects via unimodal and multimodal measures. The results of the multimodal classification were superior to those of unimodal approaches, i.e., we achieved 81% accuracy in separating psychophysiological vs. control, 87% for paradoxical vs. control, and 89% for paradoxical vs. psychophysiological insomnia. This preliminary study provides evidence that structural and functional brain data can help to distinguish two common subtypes of ID from each other and healthy subjects. These initial findings may stimulate further research to identify the underlying mechanism of each subtype and develop personalized treatments for ID in the future. Full article
(This article belongs to the Special Issue Sleep Disorders and Neural Networks)
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