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Diagnostics
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Diagnosis and Management of Sleep Disorders 2025
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Diagnosis and Management of Sleep Disorders 2025

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Pathology and Molecular Diagnostics".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 4171

Special Issue Editor

Prof. Dr. Rayleigh Ping Ying Chiang

E-Mail Website
Guest Editor
Department of Otolaryngology Head and Neck Surgery, Sleep Center, School of Medicine, China Medical University, Taichung, Taiwan
Interests: obstructive sleep apnea; sleep disorders; polysomnography; hearing loss; endoscopic surgery; sleep medicine; otolaryngology; sleep, memory and learning; EEG signal processing; electroencephalography; neurodegeneration; public health

Special Issue Information

Dear Colleagues,

This Special Issue, "Diagnosis and Management of Sleep Disorders 2024", will provide an in-depth analysis of the latest research on the diagnosis and management of sleep disorders. Articles discussing the various sleep disorders and their causes, symptoms, and diagnosis options are welcome. This Special Issue will also highlight the importance of recognizing sleep disorders in different populations and the role of various health care professionals in managing these disorders. It is designed to assist health care professionals in improving the diagnosis and management of sleep disorders and enhancing patient outcomes.

Prof. Dr. Rayleigh Ping Ying Chiang
Guest Editor

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. Diagnostics is an international peer-reviewed open access semimonthly 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 2600 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 disorders
  • obstructive sleep apnea
  • diagnosis
  • causes
  • symptoms

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

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20 pages, 7421 KiB  
Article
SLA-MLP: Enhancing Sleep Stage Analysis from EEG Signals Using Multilayer Perceptron Networks
by Farah Mohammad and Khulood Mohammed Al Mansoor
Diagnostics 2024, 14(23), 2657; https://doi.org/10.3390/diagnostics14232657 - 25 Nov 2024
Viewed by 894
Abstract
Background/Objectives: Sleep stage analysis is considered to be the key factor for understanding and diagnosing various sleep disorders, as it provides insights into sleep quality and overall health. Methods: Traditional methods of sleep stage classification, such as manual scoring and basic [...] Read more.
Background/Objectives: Sleep stage analysis is considered to be the key factor for understanding and diagnosing various sleep disorders, as it provides insights into sleep quality and overall health. Methods: Traditional methods of sleep stage classification, such as manual scoring and basic machine learning approaches, often suffer from limitations including subjective biases, limited scalability, and inadequate accuracy. Existing deep learning models have improved the accuracy of sleep stage classification but still face challenges such as overfitting, computational inefficiencies, and difficulties in handling imbalanced datasets. To address these challenges, we propose the Sleep Stage Analysis with Multilayer Perceptron (SLA-MLP) model. Results: SLA-MLP leverages advanced deep learning techniques to enhance the classification of sleep stages from EEG signals. The key steps of this approach include data collection, where diverse and high-quality EEG data are gathered; preprocessing, which involves signal cropping, spectrogram conversion, and normalization to prepare the data for analysis; data balancing, where class weights are adjusted to address any imbalances in the dataset; feature extraction, utilizing Temporal Convolutional Networks (TCNs) to extract meaningful features from the EEG signals; and final classification, applying a Multilayer Perceptron (MLP) to accurately predict sleep stages. Conclusions: SLA-MLP demonstrates superior performance compared to traditional methods by effectively addressing the limitations of existing models. Its robust preprocessing techniques, advanced feature extraction, and adaptive data balancing strategies collectively contribute to obtaining more accurate results, having an accuracy of 97.23% for the S-DSI, 96.23 for the S-DSII and 97.23% for the S-DSIII dataset. This model offers a significant advancement in the field, providing a more precise tool for sleep research and clinical applications. Full article
(This article belongs to the Special Issue Diagnosis and Management of Sleep Disorders 2025)
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17 pages, 2108 KiB  
Article
Automatic Wake and Deep-Sleep Stage Classification Based on Wigner–Ville Distribution Using a Single Electroencephalogram Signal
by Po-Liang Yeh, Murat Ozgoren, Hsiao-Ling Chen, Yun-Hong Chiang, Jie-Ling Lee, Yi-Chen Chiang and Rayleigh Ping-Ying Chiang
Diagnostics 2024, 14(6), 580; https://doi.org/10.3390/diagnostics14060580 - 8 Mar 2024
Cited by 1 | Viewed by 1810
Abstract
This research paper outlines a method for automatically classifying wakefulness and deep sleep stage (N3) based on the American Academy of Sleep Medicine (AASM) standards. The study employed a single-channel EEG signal, leveraging the Wigner–Ville Distribution (WVD) for time–frequency analysis to determine EEG [...] Read more.
This research paper outlines a method for automatically classifying wakefulness and deep sleep stage (N3) based on the American Academy of Sleep Medicine (AASM) standards. The study employed a single-channel EEG signal, leveraging the Wigner–Ville Distribution (WVD) for time–frequency analysis to determine EEG energy per second in specific frequency bands (δ, θ, α, and entire band). Particle Swarm Optimization (PSO) was used to optimize thresholds for distinguishing between wakefulness and stage N3. This process aims to mimic a sleep technician’s visual scoring but in an automated fashion, with features and thresholds extracted to classify epochs into correct sleep stages. The study’s methodology was validated using overnight PSG recordings from 20 subjects, which were evaluated by a technician. The PSG setup followed the 10–20 standard system with varying sampling rates from different hospitals. Two baselines, T1 for the wake stage and T2 for the N3 stage, were calculated using PSO to ascertain the best thresholds, which were then used to classify EEG epochs. The results showed high sensitivity, accuracy, and kappa coefficient, indicating the effectiveness of the classification algorithm. They suggest that the proposed method can reliably determine sleep stages, being aligned closely with the AASM standards and offering an intuitive approach. The paper highlights the strengths of the proposed method over traditional classifiers and expresses the intentions to extend the algorithm to classify all sleep stages in the future. Full article
(This article belongs to the Special Issue Diagnosis and Management of Sleep Disorders 2025)
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Review

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17 pages, 2379 KiB  
Review
The Impact of Diagnostic Imaging on Obstructive Sleep Apnea: Feedback from a Narrative Review
by Salvatore Lavalle, Alberto Caranti, Giannicola Iannella, Annalisa Pace, Mario Lentini, Antonino Maniaci, Ruggero Campisi, Luigi La Via, Caterina Giannitto, Edoardo Masiello, Claudio Vicini and Daniela Messineo
Diagnostics 2025, 15(3), 238; https://doi.org/10.3390/diagnostics15030238 - 21 Jan 2025
Viewed by 941
Abstract
Obstructive Sleep Apnea is a prevalent sleep disorder characterized by repeated episodes of partial or complete upper airway obstruction during sleep, leading to disrupted sleep and associated comorbidities. Effective, traditional diagnostic methods, such as polysomnography, have limitations in providing comprehensive anatomical detail. Recent [...] Read more.
Obstructive Sleep Apnea is a prevalent sleep disorder characterized by repeated episodes of partial or complete upper airway obstruction during sleep, leading to disrupted sleep and associated comorbidities. Effective, traditional diagnostic methods, such as polysomnography, have limitations in providing comprehensive anatomical detail. Recent advancements in imaging technology have the potential to revolutionize the diagnosis and management of OSA, offering detailed insights into airway anatomy, function, and dynamics. This paper explores the latest innovations in imaging modalities, including high-resolution magnetic resonance imaging, functional MRI, three-dimensional airway reconstructions, and the integration of artificial intelligence algorithms for enhanced image analysis. We discuss the potential of these technologies to improve the precision of OSA diagnosis, tailor treatment strategies, and predict treatment outcomes. Moreover, we examine the challenges of implementing these advanced imaging techniques in clinical practice, such as cost, accessibility, and the need for validation in diverse patient populations. We also consider the ethical implications of widespread imaging, particularly regarding data security and patient privacy. The future of OSA management is poised for transformation as these imaging technologies promise to provide a more nuanced understanding of the disorder and facilitate personalized treatment approaches. This paper calls for continued research and collaboration across disciplines to ensure these innovations lead to improved patient care and outcomes in the field of sleep medicine. Full article
(This article belongs to the Special Issue Diagnosis and Management of Sleep Disorders 2025)
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