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Diagnostics
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Machine Learning Approaches for the Diagnosis of Sleep and Respiratory...
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Machine Learning Approaches for the Diagnosis of Sleep and Respiratory Disorders

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 30 September 2026 | Viewed by 1275

Editors

Dr. Alaa Sheta

E-Mail Website
Guest Editor
Department of Computer Science, Southern Connecticut University, New Haven, CT 06515, USA
Interests: sleep apnea; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Shyam Subramanian

E-Mail Website
Guest Editor
Pulmonary Critical Care & Sleep Medicine, Sutter Health, Tracy, CA, USA
Interests: asthma care; bronchiectasis; chronic obstructive pulmonary disease (COPD); lung disease; pulmonary fibrosis; pulmonary nodules; sleep apnea
Special Issues, Collections and Topics in MDPI journals
Dr. Salim R. Surani

E-Mail Website
Guest Editor
Pulmonary, Critical Care & Pharmacy, Texas A&M University, College Station, TX 79016, USA
Interests: asthma; COPD; sleep medicine; quality assurance programs; long term acute care and pulmonary infections
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite researchers and experts to submit their original research papers, review articles, and case studies for a Special Issue, entitled “Machine Learning Approaches for the Diagnosis of Sleep and Respiratory Disorders”, of the MDPI journal Diagnostics (ISSN 2075-4418, https://www.mdpi.com/journal/diagnostics).

This Special Issue will focus on the latest research advancements in the application of machine and deep learning techniques for the diagnosis, prediction, and phenotyping of sleep and respiratory disorders. The intricate link between sleep and respiration is well-established; conditions such as sleep apnea not only disrupt sleep but are also closely associated with respiratory diseases such as asthma, COPD, and bronchiectasis. Machine learning and deep learning offer unprecedented potential to unravel this complexity, enabling early detection and accurate diagnosis for a broad spectrum of conditions.

This Special Issue aims to showcase innovative research that leverages computational intelligence to address diagnostic challenges in both sleep and respiratory medicine. Topics of interest include, but are not limited to, the following:

Diagnosis and Prediction of Sleep and Respiratory Disorders:

  • Automated detection and classification of sleep apnea events and other sleep-related breathing disorders using polysomnography (PSG) or other signals;
  • Machine learning models for diagnosing, phenotyping, and predicting exacerbation risk in asthma, COPD, bronchiectasis, and other chronic respiratory diseases.

Advanced Data Analysis for Diagnosis:

  • Analysis of multi-modal data for diagnostic purposes, including polysomnography, actigraphy, medical imaging, pulmonary function tests, and data from wearable technologies;
  • Identification of novel diagnostic biomarkers and risk factors from large-scale clinical and demographic datasets.

Signal Processing and Novel Diagnostic Applications:

  • Automatic analysis of physiological signals (e.g., snoring sounds, oxygen saturation, respiratory effort, and cough sounds) for disorder detection and classification;
  • Development of accessible screening tools for high-risk populations (e.g., individuals with hypertension, diabetes, or cardiovascular disease).

Evaluation and Clinical Validation of Diagnostic Models:

  • Evaluation and comparison of different machine learning models for diagnostic applications;
  • Clinical studies and case reports validating the diagnostic accuracy and utility of AI models in sleep and respiratory medicine.

All submissions will be peer-reviewed. Accepted papers will be published in this Special Issue of Diagnostics, contributing to a growing body of knowledge at the intersection of AI and clinical medicine. Please ensure your submission conforms to the journal's guidelines and formatting requirements.

We look forward to receiving your contributions and advancing the diagnostic capabilities in sleep and respiratory medicine through the power of machine learning and deep learning.

Dr. Alaa Sheta
Dr. Shyam Subramanian
Dr. Salim R. Surani
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • diagnostics
  • artificial intelligence
  • sleep
  • respiratory disorders
  • healthcare

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

Published Papers (2 papers)

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Research

16 pages, 1918 KB  
Article
Development and Explainable Machine Learning Validation of a Novel Sleep Disturbance Ratio for Obstructive Sleep Apnea Severity Assessment
by Mehmet Kabak, Halit Irmak, Abdullah Reşit Kılıç and Barış Çil
Diagnostics 2026, 16(16), 2606; https://doi.org/10.3390/diagnostics16162606 - 17 Aug 2026
Viewed by 152
Abstract
Background/Objectives: Obstructive sleep apnea (OSA) is traditionally classified according to the apnea–hypopnea index (AHI), although AHI alone does not fully capture the heterogeneity of disease severity. This study introduces a novel polysomnography-derived biomarker, the Sleep Disturbance Ratio (SDR), and evaluates its contribution [...] Read more.
Background/Objectives: Obstructive sleep apnea (OSA) is traditionally classified according to the apnea–hypopnea index (AHI), although AHI alone does not fully capture the heterogeneity of disease severity. This study introduces a novel polysomnography-derived biomarker, the Sleep Disturbance Ratio (SDR), and evaluates its contribution to OSA severity classification using explainable machine learning approaches. Methods: A retrospective study was conducted using polysomnographic data from 767 adults who underwent overnight sleep studies. SDR was calculated as the logarithmic ratio between light sleep (N1 + N2) and restorative sleep (N3 + REM). Predictive models were trained and evaluated using four established machine learning algorithms: Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN). Model performance was assessed using accuracy, Cohen’s kappa, F1-score, multiclass AUC, ROC analysis, feature importance ranking, partial dependence plots, and clinical risk mapping. Results: Although SDR did not differ significantly across conventional OSA severity groups in univariate analysis (p = 0.77), explainable machine learning analyses consistently demonstrated that increasing SDR was associated with a higher probability of severe OSA, particularly in combination with lower mean oxygen saturation. SDR also showed strong physiological relevance by correlating positively with N2 sleep (r = 0.85) and negatively with N3 sleep (r = −0.83), supporting its role as a biomarker of sleep fragmentation. Among the predictive models, Random Forest achieved the highest classification accuracy (75.0%), whereas XGBoost demonstrated the best multiclass discrimination (AUC = 0.895) and the highest ROC performance for severe OSA (AUC = 0.962). ESS remained the most influential predictor across all models. Conclusions: This study introduces SDR as a novel polysomnography-derived biomarker that captures sleep architecture disruption beyond conventional AHI-based assessment. Although SDR is not an independent diagnostic marker, explainable machine learning analyses demonstrated that it provides complementary physiological information for OSA severity classification, particularly when integrated with oxygenation parameters. Full article
(This article belongs to the Special Issue Machine Learning Approaches for the Diagnosis of Sleep and Respiratory Disorders)
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35 pages, 7577 KB  
Article
Early Screening of Sleep-Disordered Breathing Using Metaheuristic-Optimized Extreme Learning Machines
by Thaer Thaher, Alaa Sheta, Huthaifa I. Ashqar, Hamouda Chantar and Salim Surani
Diagnostics 2026, 16(13), 2050; https://doi.org/10.3390/diagnostics16132050 - 30 Jun 2026
Viewed by 280
Abstract
Background/Objectives: Obstructive sleep apnea (OSA) is a common and serious sleep-related disorder that causes repeated interruptions in breathing during sleep. Traditional diagnostic methods, such as polysomnography, are accurate but costly, time-consuming, and unsuitable for large-scale screening. This study proposes and evaluates a [...] Read more.
Background/Objectives: Obstructive sleep apnea (OSA) is a common and serious sleep-related disorder that causes repeated interruptions in breathing during sleep. Traditional diagnostic methods, such as polysomnography, are accurate but costly, time-consuming, and unsuitable for large-scale screening. This study proposes and evaluates a lightweight diagnostic framework based on an Extreme Learning Machine (ELM) optimized by a set of basic and advanced metaheuristic optimizers. The model aims to evaluate whether metaheuristic optimization can improve ELM-based classification performance using structured demographic, clinical, and sleep-related predictors. Methods: Two real datasets were employed to train and evaluate the proposed framework: (i) a clinical OSA dataset with 274 subjects and 31 demographic/anthropometric and sleep-related predictors, and (ii) a public strongly imbalanced Sleep-Disordered Breathing (SDB) dataset with 500 subjects and 10 structured predictors. Metaheuristic algorithms are used to optimize ELM weights and biases, addressing the instability of random initialization and improving model generalization. The optimized models are evaluated against eight baseline classifiers, including logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), XGBoost (XGB), and a standard ELM classifier. Results: Results show that metaheuristic optimization moderately improves ELM on the OSA dataset, increasing ROC-AUC from 0.6527 to about 0.73 and accuracy from 0.6573 to about 0.69–0.70, while on the highly imbalanced SDB dataset, it yields modest ROC-AUC gains (from 0.5132 to about 0.544–0.548) with small decreases in accuracy and F1-score. We additionally assess class-imbalance handling on the SDB dataset and analyze feature importance with permutation importance and SHAP, which shows the models rely heavily on diagnosis-derived predictors. Conclusions: The proposed framework provides a lightweight ELM-based decision-support approach with low inference cost after offline optimization. The results suggest potential value for screening-oriented OSA/SDB classification, but further validation with larger cohorts and a screening-only feature set is needed before clinical implementation. Full article
(This article belongs to the Special Issue Machine Learning Approaches for the Diagnosis of Sleep and Respiratory Disorders)
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