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Diagnostics
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Diagnosis of Sleep Disorders Using Machine Learning Approaches
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Diagnosis of Sleep Disorders Using Machine Learning Approaches

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 2024 | Viewed by 7637

Special Issue 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. Salim R. Surani

E-Mail Website
Guest Editor
Department of Medicine, Texas A&M University, Corpus Christi, TX, USA
Interests: asthma; COPD; sleep medicine; quality assurance programs; long term acute care and pulmonary infections
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 Issue Information

Dear Colleagues,

We invite researchers and experts to submit their original research papers, review articles, and case studies for a Special Issue, Diagnosis of Sleep Disorders Using Machine Learning Approaches, 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 treatment of sleep disorders. Sleep apnea is a common sleep disorder that affects millions of people worldwide. The disorder is characterized by frequent interruptions in breathing during sleep, leading to poor quality sleep and a range of health problems, including cardiovascular disease, diabetes, and stroke. Machine and deep learning techniques have shown great potential for the early detection and diagnosis of sleep apnea and for developing personalized treatment plans.

This Special Issue aims to focus on the utilization of machine and deep learning to solve the abovementioned problem. This includes (but is not limited to) the following topics:

  • Diagnosis and prediction of sleep apnea;
  • Automatic detection of sleep apnea events utilizing polysomnography (PSG) data;
  • Analysis of sleep data, including polysomnography, actigraphy, and other wearable technologies;
  • Development of personalized treatment plans for sleep apnea;
  • Prediction of the severity of sleep apnea based on PSG data, clinical features, and demographic information;
  • Analysis of large datasets and identify risk factors for sleep apnea, such as obesity, smoking, or alcohol consumption;
  • Optimization of the treatment for sleep apnea, such as selecting the most effective type of positive airway pressure (PAP) therapy for individual patients;
  • Integration of machine and deep learning with other diagnostic techniques, such as imaging and biomarkers;
  • Evaluation and comparison of different sleep apnea diagnoses and treatment;
  • Automatic analysis of signals related to sleep apnea, such as snoring sounds or oxygen saturation levels, to detect and classify sleep apnea events;
  • Sleep apnea screening in high-risk populations for individuals with hypertension, diabetes, or cardiovascular disease;
  • Clinical studies and case reports for sleep apnea management.

All submissions will be peer reviewed, and the accepted papers will be published in a Special Issue of a reputable journal. Please ensure your submission conforms to the journal's guidelines and formatting requirements.

We look forward to receiving your submissions and advancing the sleep apnea diagnosis and treatment field with machine and deep learning techniques.

Sincerely,

Dr. Alaa Sheta
Dr. Salim R. Surani
Dr. Shyam Subramanian
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. 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.

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (5 papers)

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Research

22 pages, 1617 KiB  
Article
Combining Signals for EEG-Free Arousal Detection during Home Sleep Testing: A Retrospective Study
by Safa Boudabous, Juliette Millet and Emmanuel Bacry
Diagnostics 2024, 14(18), 2077; https://doi.org/10.3390/diagnostics14182077 - 19 Sep 2024
Viewed by 268
Abstract
Introduction: Accurately detecting arousal events during sleep is essential for evaluating sleep quality and diagnosing sleep disorders, such as sleep apnea/hypopnea syndrome. While the American Academy of Sleep Medicine guidelines associate arousal events with electroencephalogram (EEG) signal variations, EEGs are often not recorded [...] Read more.
Introduction: Accurately detecting arousal events during sleep is essential for evaluating sleep quality and diagnosing sleep disorders, such as sleep apnea/hypopnea syndrome. While the American Academy of Sleep Medicine guidelines associate arousal events with electroencephalogram (EEG) signal variations, EEGs are often not recorded during home sleep testing (HST) using wearable devices or smartphone applications. Objectives: The primary objective of this study was to explore the potential of alternatively relying on combinations of easily measurable physiological signals during HST for arousal detection where EEGs are not recorded. Methods: We conducted a data-driven retrospective study following an incremental device-agnostic analysis approach, where we simulated a limited-channel setting using polysomnography data and used deep learning to automate the detection task. During the analysis, we tested multiple signal combinations to evaluate their potential effectiveness. We trained and evaluated the model on the Multi-Ethnic Study of Atherosclerosis dataset. Results: The results demonstrated that combining multiple signals significantly improved performance compared with single-input signal models. Notably, combining thoracic effort, heart rate, and a wake/sleep indicator signal achieved competitive performance compared with the state-of-the-art DeepCAD model using electrocardiogram as input with an average precision of 61.59% and an average recall of 56.46% across the test records. Conclusions: This study demonstrated the potential of combining easy-to-record HST signals to characterize the autonomic markers of arousal better. It provides valuable insights to HST device designers on signals that improve EEG-free arousal detection. Full article
(This article belongs to the Special Issue Diagnosis of Sleep Disorders Using Machine Learning Approaches)
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14 pages, 4036 KiB  
Article
A Comprehensive Study on a Deep-Learning-Based Electrocardiography Analysis for Estimating the Apnea-Hypopnea Index
by Seola Kim, Hyun-Soo Choi, Dohyun Kim, Minkyu Kim, Seo-Young Lee, Jung-Kyeom Kim, Yoon Kim and Woo Hyun Lee
Diagnostics 2024, 14(11), 1134; https://doi.org/10.3390/diagnostics14111134 - 29 May 2024
Viewed by 686
Abstract
This study introduces a deep-learning-based automatic sleep scoring system to detect sleep apnea using a single-lead electrocardiography (ECG) signal, focusing on accurately estimating the apnea–hypopnea index (AHI). Unlike other research, this work emphasizes AHI estimation, crucial for the diagnosis and severity evaluation of [...] Read more.
This study introduces a deep-learning-based automatic sleep scoring system to detect sleep apnea using a single-lead electrocardiography (ECG) signal, focusing on accurately estimating the apnea–hypopnea index (AHI). Unlike other research, this work emphasizes AHI estimation, crucial for the diagnosis and severity evaluation of sleep apnea. The suggested model, trained on 1465 ECG recordings, combines the deep-shallow fusion network for sleep apnea detection network (DSF-SANet) and gated recurrent units (GRUs) to analyze ECG signals at 1-min intervals, capturing sleep-related respiratory disturbances. Achieving a 0.87 correlation coefficient with actual AHI values, an accuracy of 0.82, an F1 score of 0.71, and an area under the receiver operating characteristic curve of 0.88 for per-segment classification, our model was effective in identifying sleep-breathing events and estimating the AHI, offering a promising tool for medical professionals. Full article
(This article belongs to the Special Issue Diagnosis of Sleep Disorders Using Machine Learning Approaches)
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12 pages, 2002 KiB  
Article
Periodic Leg Movements during Sleep Associated with REM Sleep Behavior Disorder: A Machine Learning Study
by Maria Salsone, Basilio Vescio, Andrea Quattrone, Sara Marelli, Alessandra Castelnuovo, Francesca Casoni, Aldo Quattrone and Luigi Ferini-Strambi
Diagnostics 2024, 14(4), 363; https://doi.org/10.3390/diagnostics14040363 - 7 Feb 2024
Cited by 2 | Viewed by 1203
Abstract
Most patients with idiopathic REM sleep behavior disorder (iRBD) present peculiar repetitive leg jerks during sleep in their clinical spectrum, called periodic leg movements (PLMS). The clinical differentiation of iRBD patients with and without PLMS is challenging, without polysomnographic confirmation. The aim of [...] Read more.
Most patients with idiopathic REM sleep behavior disorder (iRBD) present peculiar repetitive leg jerks during sleep in their clinical spectrum, called periodic leg movements (PLMS). The clinical differentiation of iRBD patients with and without PLMS is challenging, without polysomnographic confirmation. The aim of this study is to develop a new Machine Learning (ML) approach to distinguish between iRBD phenotypes. Heart rate variability (HRV) data were acquired from forty-two consecutive iRBD patients (23 with PLMS and 19 without PLMS). All participants underwent video-polysomnography to confirm the clinical diagnosis. ML models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were trained on HRV data, and classification performances were assessed using Leave-One-Out cross-validation. No significant clinical differences emerged between the two groups. The RF model showed the best performance in differentiating between iRBD phenotypes with excellent accuracy (86%), sensitivity (96%), and specificity (74%); SVM and XGBoost had good accuracy (81% and 78%, respectively), sensitivity (83% for both), and specificity (79% and 72%, respectively). In contrast, LR had low performances (accuracy 71%). Our results demonstrate that ML algorithms accurately differentiate iRBD patients from those without PLMS, encouraging the use of Artificial Intelligence to support the diagnosis of clinically indistinguishable iRBD phenotypes. Full article
(This article belongs to the Special Issue Diagnosis of Sleep Disorders Using Machine Learning Approaches)
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19 pages, 1521 KiB  
Article
Potential of Machine Learning for Predicting Sleep Disorders: A Comprehensive Analysis of Regression and Classification Models
by Raed Alazaidah, Ghassan Samara, Mohammad Aljaidi, Mais Haj Qasem, Ayoub Alsarhan and Mohammed Alshammari
Diagnostics 2024, 14(1), 27; https://doi.org/10.3390/diagnostics14010027 - 22 Dec 2023
Cited by 1 | Viewed by 2200
Abstract
Sleep disorder is a disease that can be categorized as both an emotional and physical problem. It imposes several difficulties and problems, such as distress during the day, sleep-wake disorders, anxiety, and several other problems. Hence, the main objective of this research was [...] Read more.
Sleep disorder is a disease that can be categorized as both an emotional and physical problem. It imposes several difficulties and problems, such as distress during the day, sleep-wake disorders, anxiety, and several other problems. Hence, the main objective of this research was to utilize the strong capabilities of machine learning in the prediction of sleep disorders. In specific, this research aimed to meet three main objectives. These objectives were to identify the best regression model, the best classification model, and the best learning strategy that highly suited sleep disorder datasets. Considering two related datasets and several evaluation metrics that were related to the tasks of regression and classification, the results revealed the superiority of the MultilayerPerceptron, SMOreg, and KStar regression models compared with the other twenty three regression models. Furthermore, IBK, RandomForest, and RandomizableFilteredClassifier showed superior performance compared with other classification models that belonged to several learning strategies. Finally, the Function learning strategy showed the best predictive performance among the six considered strategies in both datasets and with respect to the most evaluation metrics. Full article
(This article belongs to the Special Issue Diagnosis of Sleep Disorders Using Machine Learning Approaches)
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28 pages, 1310 KiB  
Article
Diagnosis of Obstructive Sleep Apnea Using Feature Selection, Classification Methods, and Data Grouping Based Age, Sex, and Race
by Alaa Sheta, Thaer Thaher, Salim R. Surani, Hamza Turabieh, Malik Braik, Jingwei Too, Noor Abu-El-Rub, Majdi Mafarjah, Hamouda Chantar and Shyam Subramanian
Diagnostics 2023, 13(14), 2417; https://doi.org/10.3390/diagnostics13142417 - 20 Jul 2023
Cited by 4 | Viewed by 2191
Abstract
Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3–7% of males and 2–5% of females. In the United States alone, 50–70 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, [...] Read more.
Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3–7% of males and 2–5% of females. In the United States alone, 50–70 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, thereby leading to adverse effects such as daytime sleepiness, cognitive impairment, and reduced concentration. It also contributes to an increased risk of cardiovascular conditions and adversely impacts patient overall quality of life. As a result, numerous researchers have focused on developing automated detection models to identify OSA and address these limitations effectively and accurately. This study explored the potential benefits of utilizing machine learning methods based on demographic information for diagnosing the OSA syndrome. We gathered a comprehensive dataset from the Torr Sleep Center in Corpus Christi, Texas, USA. The dataset comprises 31 features, including demographic characteristics such as race, age, sex, BMI, Epworth score, M. Friedman tongue position, snoring, and more. We devised a novel process encompassing pre-processing, data grouping, feature selection, and machine learning classification methods to achieve the research objectives. The classification methods employed in this study encompass decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), and subspace discriminant (Ensemble) classifiers. Through rigorous experimentation, the results indicated the superior performance of the optimized kNN and SVM classifiers for accurately classifying sleep apnea. Moreover, significant enhancements in model accuracy were observed when utilizing the selected demographic variables and employing data grouping techniques. For instance, the accuracy percentage demonstrated an approximate improvement of 4.5%, 5%, and 10% with the feature selection approach when applied to the grouped data of Caucasians, females, and individuals aged 50 or below, respectively. Furthermore, a comparison with prior studies confirmed that effective data grouping and proper feature selection yielded superior performance in OSA detection when combined with an appropriate classification method. Overall, the findings of this research highlight the importance of leveraging demographic information, employing proper feature selection techniques, and utilizing optimized classification models for accurate and efficient OSA diagnosis. Full article
(This article belongs to the Special Issue Diagnosis of Sleep Disorders Using Machine Learning Approaches)
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