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Moving beyond Current Diagnosis of Sleep-Disordered Breathing

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 14741

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Special Issue Editors

Dr. Erna Sif Arnardóttir

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Guest Editor

1. Department of Engineering, Reykjavik University, Reykjavik, Iceland
2. Department of Computer Science, Reykjavik University, Reykjavik, Iceland
3. Landspitali – The National University Hospital of Iceland, Reykjavik, Iceland
Interests: sleep; sleep-disordered breathing; obstructive sleep apnea; snoring; sleep disorders; physiology; biosignal analysis; machine learning; wearables; subjective analysis

Dr. Timo Leppänen

E-Mail Website
Guest Editor

1. Department of Applied Physics, University of Eastern Finland, 70210 Kuopio, Finland
2. Diagnostic Imaging Center, Kuopio University Hospital, 70210 Kuopio, Finland
3. School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, QLD 4072, Australia
Interests: sleep apnea; sleep-disordered breathing; biosignal analysis; machine learning; wearables

Special Issue Information

Dear Colleagues,

We are pleased to invite you to contribute the Special Issue *Moving beyond Current Diagnosis of Sleep-Disordered Breathing*, to be published in Diagnostics.

Sleep-disordered breathing (SDB) is a huge global burden for affected individuals and society as a whole.

This Special Issue aims to seek novel pathways, technological solutions, and analytical approaches for more accurate monitoring, detection, and quantification of SDB to replace the current outdated, expensive, and labor-intensive clinical practices.

Both original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

The use of machine learning to analyze sleep data and estimate the risk for symptoms and adverse health consequences;
Novel pathways to monitor and diagnose SDB;
Wearable technologies for long-term monitoring of SDB and diagnosis;
Novel subjective and objective methods to estimate the effect of SDB on patient well-being.

We look forward to receiving your contributions.

Dr. Erna Sif Arnardóttir
Dr. Timo Leppänen
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.

Keywords

sleep apnea diagnosis
self-applied sleep studies
long-term monitoring
subjective and objective measurements
patient-reported outcome measures
home sleep apnea testing
wearables
non-wearables

Published Papers (10 papers)

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Research

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20 pages, 2660 KiB

Open AccessArticle

Prediction of the Sleep Apnea Severity Using 2D-Convolutional Neural Networks and Respiratory Effort Signals

by Verónica Barroso-García, Marta Fernández-Poyatos, Benjamín Sahelices, Daniel Álvarez, David Gozal, Roberto Hornero and Gonzalo C. Gutiérrez-Tobal

Diagnostics 2023, 13(20), 3187; https://doi.org/10.3390/diagnostics13203187 - 12 Oct 2023

Cited by 1 | Viewed by 1033

Abstract

The high prevalence of sleep apnea and the limitations of polysomnography have prompted the investigation of strategies aimed at automated diagnosis using a restricted number of physiological measures. This study aimed to demonstrate that thoracic (THO) and abdominal (ABD) movement signals are useful for accurately estimating the severity of sleep apnea, even if central respiratory events are present. Thus, we developed 2D-convolutional neural networks (CNNs) jointly using THO and ABD to automatically estimate sleep apnea severity and evaluate the central event contribution. Our proposal achieved an intraclass correlation coefficient (ICC) = 0.75 and a root mean square error (RMSE) = 10.33 events/h when estimating the apnea-hypopnea index, and ICC = 0.83 and RMSE = 0.95 events/h when estimating the central apnea index. The CNN obtained accuracies of 94.98%, 79.82%, and 81.60% for 5, 15, and 30 events/h when evaluating the complete apnea hypopnea index. The model improved when the nature of the events was central: 98.72% and 99.74% accuracy for 5 and 15 events/h. Hence, the information extracted from these signals using CNNs could be a powerful tool to diagnose sleep apnea, especially in subjects with a high density of central apnea events. Full article

(This article belongs to the Special Issue Moving beyond Current Diagnosis of Sleep-Disordered Breathing)

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14 pages, 713 KiB

Open AccessArticle

Evaluating User Compliance in Mobile Health Apps: Insights from a 90-Day Study Using a Digital Sleep Diary

by Hlín Kristbergsdottir, Lisa Schmitz, Erna Sif Arnardottir and Anna Sigridur Islind

Diagnostics 2023, 13(18), 2883; https://doi.org/10.3390/diagnostics13182883 - 8 Sep 2023

Viewed by 1049

Abstract

Sleep diaries are the gold standard for subjective assessment of sleep variables in clinical practice. Digitization of sleep diaries is needed, as paper versions are prone to human error, memory bias, and difficulties monitoring compliance. Methods: 45 healthy eligible participants (M^age = 50.3 years, range 23–74, 56% female) were asked to use a sleep diary mobile app for 90 consecutive days. Univariate and bivariate analysis was used for group comparison and linear regression for analyzing reporting trends and compliance over time. Results: Overall compliance was high in the first two study months but tended to decrease over time (p < 0.001). Morning and evening diary entries were highly correlated (r = 0.932, p < 0.001) and participants significantly answered on average 4.1 days (95% CI [1.7, 6.6]) more often in the morning (M = 60.2, sd = 22.1) than evening ((M = 56.1, sd = 22.2), p < 0.001). Conclusion: Using a daily diary assessment in a longitudinal sleep study with a sleep diary delivered through a mobile application was feasible, and compliance in this study was satisfactory. Full article

(This article belongs to the Special Issue Moving beyond Current Diagnosis of Sleep-Disordered Breathing)

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11 pages, 872 KiB

Open AccessArticle

Heart Rate Variability as a Surrogate Marker of Severe Chronic Coronary Syndrome in Patients with Obstructive Sleep Apnea

by Christopher Seifen, Maria Zisiopoulou, Katharina Ludwig, Johannes Pordzik, Muthuraman Muthuraman and Haralampos Gouveris

Diagnostics 2023, 13(17), 2838; https://doi.org/10.3390/diagnostics13172838 - 1 Sep 2023

Viewed by 1028

Abstract

Background and Objectives: Obstructive sleep apnea (OSA) is a known risk factor for chronic coronary syndrome (CCS). CCS and OSA are separately associated with significant changes in heart rate variability (HRV). In this proof-of-concept study, we tested whether HRV values are significantly different between OSA patients with concomitant severe CCS, and OSA patients without known CCS. Material and Methods: The study comprised a retrospective assessment of the historical and raw polysomnography (PSG) data of 32 patients who presented to a tertiary university hospital with clinical complaints of OSA. A total of 16 patients (four females, mean age 62.94 ± 2.74 years, mean body mass index (BMI) 31.93 ± 1.65 kg/m²) with OSA (median apnea-hypopnea index (AHI) 39.1 (30.5–70.6)/h) and severe CCS were compared to 16 patients (four females, mean age 62.35 ± 2.06 years, mean BMI 32.19 ± 1.07 kg/m²) with OSA (median AHI 40 (30.6–44.5)/h) but without severe CCS. The short–long-term HRV (in msec) was calculated based on the data of a single-lead electrocardiogram (ECG) provided by one full-night PSG, using the standard deviation of the NN, normal-to-normal intervals (SDNN) and the heart rate variability triangular index (HRVI) methods, and compared between the two groups. Results: A significant reduction (p < 0.05) in both SDNN and HRVI was found in the OSA group with CCS compared to the OSA group without CCS. Conclusions: Severe CCS has a significant impact on short–long-term HRV in OSA patients. Further studies in OSA patients with less-severe CCS may shed more light onto the involved mechanistic processes. If confirmed in future larger studies, this physiologic metric has the potential to provide a robust surrogate marker of severe CCS in OSA patients. Full article

(This article belongs to the Special Issue Moving beyond Current Diagnosis of Sleep-Disordered Breathing)

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15 pages, 999 KiB

Open AccessArticle

Sleep-Disordered Breathing and Prognosis after Ischemic Stroke: It Is Not Apnea-Hypopnea Index That Matters

by Lyudmila Korostovtseva, Mikhail Bochkarev, Valeria Amelina, Uliana Nikishkina, Sofia Osipenko, Anastasia Vasilieva, Vladislav Zheleznyakov, Ekaterina Zabroda, Alexey Gordeev, Maria Golovkova-Kucheryavaia, Stanislav Yanishevskiy, Yurii Sviryaev and Aleksandra Konradi

Diagnostics 2023, 13(13), 2246; https://doi.org/10.3390/diagnostics13132246 - 3 Jul 2023

Cited by 1 | Viewed by 1204

Abstract

Background: Sleep-disordered breathing (SDB) is highly prevalent after stroke and is considered to be a risk factor for poor post-stroke outcomes. The aim of this observational study was to evaluate the effect of nocturnal respiratory-related indices based on nocturnal respiratory polygraphy on clinical outcomes (including mortality and non-fatal events) in patients with ischemic stroke. Methods: A total of 328 consecutive patients (181 (55%) males, mean age 65.8 ± 11.2 years old) with confirmed ischemic stroke admitted to a stroke unit within 24 h after stroke onset were included in the analysis. All patients underwent standard diagnostic and treatment procedures, and sleep polygraphy was performed within the clinical routine in the first 72 h after admission. The long-term outcomes were assessed by cumulative endpoint (death of any cause, new non-fatal myocardial infarction, new non-fatal stroke/transient ischemic attack, emergency revascularization, emergency hospitalization due to the worsening of cardiovascular disease). A Cox-regression analysis was applied to evaluate the effects of nocturnal respiratory indices on survival. Results: The mean follow-up period comprised 12 months (maximal—48 months). Patients with unfavourable outcomes demonstrated a higher obstructive apnea-hypopnea index, a higher hypoxemia burden assessed as a percent of the time with SpO₂ < 90%, a higher average desaturation drop, and a higher respiratory rate at night. Survival time was significantly lower (30.6 (26.5; 34.7) versus 37.9 (34.2; 41.6) months (Log Rank 6.857, p = 0.009)) in patients with higher hypoxemia burden (SpO₂ < 90% during ≥2.1% versus <2.1% of total analyzed time). However, survival time did not differ depending on the SDB presence assessed by AHI thresholds (either ≥5 or ≥15/h). The multivariable Cox proportional hazards regression (backward stepwise analysis) model demonstrated that the parameters of hypoxemia burden were significantly associated with survival time, independent of age, stroke severity, stroke-related medical interventions, comorbidities, and laboratory tests. Conclusion: Our study demonstrates that the indices of hypoxemia burden have additional independent predictive value for long-term outcomes (mortality and non-fatal cardiovascular events) after ischemic stroke. Full article

(This article belongs to the Special Issue Moving beyond Current Diagnosis of Sleep-Disordered Breathing)

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15 pages, 1974 KiB

Open AccessArticle

The Use of Respiratory Effort Improves an ECG-Based Deep Learning Algorithm to Assess Sleep-Disordered Breathing

by Jiali Xie, Pedro Fonseca, Johannes P. van Dijk, Xi Long and Sebastiaan Overeem

Diagnostics 2023, 13(13), 2146; https://doi.org/10.3390/diagnostics13132146 - 23 Jun 2023

Cited by 3 | Viewed by 1176

Abstract

Background: Sleep apnea is a prevalent sleep-disordered breathing (SDB) condition that affects a large population worldwide. Research has demonstrated the potential of using electrocardiographic (ECG) signals (heart rate and ECG-derived respiration, EDR) to detect SDB. However, EDR may be a suboptimal replacement for respiration signals. Methods: We evaluated a previously described ECG-based deep learning algorithm in an independent dataset including 198 patients and compared performance for SDB event detection using thoracic respiratory effort versus EDR. We also evaluated the algorithm in terms of apnea-hypopnea index (AHI) estimation performance, and SDB severity classification based on the estimated AHI. Results: Using respiratory effort instead of EDR, we achieved an improved performance in SDB event detection (F1 score = 0.708), AHI estimation (Spearman’s correlation = 0.922), and SDB severity classification (Cohen’s kappa of 0.62 was obtained based on AHI). Conclusion: Respiratory effort is superior to EDR to assess SDB. Using respiratory effort and ECG, the previously described algorithm achieves good performance in a new dataset from an independent laboratory confirming its adequacy for this task. Full article

(This article belongs to the Special Issue Moving beyond Current Diagnosis of Sleep-Disordered Breathing)

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14 pages, 2848 KiB

Open AccessArticle

Utilizing Envelope Analysis of a Nasal Pressure Signal for Sleep Apnea Severity Estimation

by Mikke Varis, Tuomas Karhu, Timo Leppänen and Sami Nikkonen

Diagnostics 2023, 13(10), 1776; https://doi.org/10.3390/diagnostics13101776 - 17 May 2023

Cited by 1 | Viewed by 1272

Abstract

Obstructive sleep apnea (OSA) severity assessment is based on manually scored respiratory events and their arbitrary definitions. Thus, we present an alternative method to objectively evaluate OSA severity independently of the manual scorings and scoring rules. A retrospective envelope analysis was conducted on 847 suspected OSA patients. Four parameters were calculated from the difference between the nasal pressure signal’s upper and lower envelopes: average (AV), median (MD), standard deviation (SD), and coefficient of variation (CoV). We computed the parameters from the entirety of the recorded signals to perform binary classifications of patients using three different apnea–hypopnea index (AHI) thresholds (5-15-30). Additionally, the calculations were undertaken in 30-second epochs to estimate the ability of the parameters to detect manually scored respiratory events. Classification performances were assessed with areas under the curves (AUCs). As a result, the SD (AUCs ≥ 0.86) and CoV (AUCs ≥ 0.82) were the best classifiers for all AHI thresholds. Furthermore, non-OSA and severe OSA patients were separated well with SD (AUC = 0.97) and CoV (AUC = 0.95). Respiratory events within the epochs were identified moderately with MD (AUC = 0.76) and CoV (AUC = 0.82). In conclusion, envelope analysis is a promising alternative method by which to assess OSA severity without relying on manual scoring or the scoring rules of respiratory events. Full article

(This article belongs to the Special Issue Moving beyond Current Diagnosis of Sleep-Disordered Breathing)

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Review

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17 pages, 2033 KiB

Open AccessReview

A Review of Novel Oximetry Parameters for the Prediction of Cardiovascular Disease in Obstructive Sleep Apnoea

by Siying He, Peter A. Cistulli and Philip de Chazal

Diagnostics 2023, 13(21), 3323; https://doi.org/10.3390/diagnostics13213323 - 26 Oct 2023

Viewed by 1487

Abstract

Obstructive sleep apnoea (OSA) is a sleep disorder with repetitive collapse of the upper airway during sleep, which leads to intermittent hypoxic events overnight, adverse neurocognitive, metabolic complications, and ultimately an increased risk of cardiovascular disease (CVD). The standard diagnostic parameter for OSA, apnoea–hypopnoea index (AHI), is inadequate to predict CVD morbidity and mortality, because it focuses only on the frequency of apnoea and hypopnoea events, and fails to reveal other physiological information for the prediction of CVD events. Novel parameters have been introduced to compensate for the deficiencies of AHI. However, the calculation methods and criteria for these parameters are unclear, hindering their use in cross-study analysis and studies. This review aims to discuss novel parameters for predicting CVD events from oximetry signals and to summarise the corresponding computational methods. Full article

(This article belongs to the Special Issue Moving beyond Current Diagnosis of Sleep-Disordered Breathing)

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16 pages, 1338 KiB

Open AccessReview

Insights into the Use of Point-of-Care Ultrasound for Diagnosing Obstructive Sleep Apnea

by Alexandros Kalkanis, Dries Testelmans, Dimitrios Papadopoulos, Annelies Van den Driessche and Bertien Buyse

Diagnostics 2023, 13(13), 2262; https://doi.org/10.3390/diagnostics13132262 - 4 Jul 2023

Cited by 1 | Viewed by 2793

Abstract

Obstructive sleep apnea (OSA) is a sleeping disorder caused by complete or partial disturbance of breathing during the night. Existing screening methods include questionnaire-based evaluations which are time-consuming, vary in specificity, and are not globally adopted. Point-of-care ultrasound (PoCUS), on the other hand, is a painless, inexpensive, portable, and useful tool that has already been introduced for the evaluation of upper airways by anesthetists. PoCUS could also serve as a potential screening tool for the diagnosis of OSA by measuring different airway parameters, including retropalatal pharynx transverse diameter, tongue base thickness, distance between lingual arteries, lateral parapharyngeal wall thickness, palatine tonsil volume, and some non-airway parameters like carotid intima–media thickness, mesenteric fat thickness, and diaphragm characteristics. This study reviewed previously reported studies to highlight the importance of PoCUS as a potential screening tool for OSA. Full article

(This article belongs to the Special Issue Moving beyond Current Diagnosis of Sleep-Disordered Breathing)

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12 pages, 2191 KiB

Open AccessReview

The Contribution of Sleep Texture in the Characterization of Sleep Apnea

by Carlotta Mutti, Irene Pollara, Anna Abramo, Margherita Soglia, Clara Rapina, Carmela Mastrillo, Francesca Alessandrini, Ivana Rosenzweig, Francesco Rausa, Silvia Pizzarotti, Marcello luigi Salvatelli, Giulia Balella and Liborio Parrino

Diagnostics 2023, 13(13), 2217; https://doi.org/10.3390/diagnostics13132217 - 29 Jun 2023

Viewed by 1556

Abstract

Obstructive sleep apnea (OSA) is multi-faceted world-wide-distributed disorder exerting deep effects on the sleeping brain. In the latest years, strong efforts have been dedicated to finding novel measures assessing the real impact and severity of the pathology, traditionally trivialized by the simplistic apnea/hypopnea index. Due to the unavoidable connection between OSA and sleep, we reviewed the key aspects linking the breathing disorder with sleep pathophysiology, focusing on the role of cyclic alternating pattern (CAP). Sleep structure, reflecting the degree of apnea-induced sleep instability, may provide topical information to stratify OSA severity and foresee some of its dangerous consequences such as excessive daytime sleepiness and cognitive deterioration. Machine learning approaches may reinforce our understanding of this complex multi-level pathology, supporting patients’ phenotypization and easing in a more tailored approach for sleep apnea. Full article

(This article belongs to the Special Issue Moving beyond Current Diagnosis of Sleep-Disordered Breathing)

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Other

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17 pages, 2227 KiB

Open AccessSystematic Review

Diagnosis and Orthodontic Treatment of Obstructive Sleep Apnea Syndrome Children—A Systematic Review

by Kenan Ferati, Arberesha Bexheti-Ferati, Andrea Palermo, Carmen Pezzolla, Irma Trilli, Roberta Sardano, Giulia Latini, Alessio Danilo Inchingolo, Angelo Michele Inchingolo, Giuseppina Malcangi, Francesco Inchingolo, Gianna Dipalma and Antonio Mancini

Diagnostics 2024, 14(3), 289; https://doi.org/10.3390/diagnostics14030289 - 29 Jan 2024

Viewed by 1364

Abstract

Obstructive sleep apnea syndrome (OSAS) is a respiratory illness that is associated with recurrent episodes of either partial or full obstruction of the upper airways, or apnea, among other sleep disorders. This study aims to analyze, through a literature review, whether orthodontic treatment can be a good treatment strategy for this type of disorder. We performed a database search on Scopus, Web of Science, and Pubmed with the keywords OSA(S) and orthodontics to select the papers under evaluation. The criteria for inclusion were articles related to OSA(S) children undergoing an orthodontic treatment and clinical studies or case series, excluding systematic reviews, narrative reviews, meta-analyses, adult studies, animal models, and in vitro studies. The screening phase ended with the selection of 16 publications for this work. RME, or rapid maxillary expansion, turned out to be the preferred orthodontic treatment in cases of pediatric OSAS. The goal of this orthodontic procedure is to increase the hard palate’s transverse diameter by reopening the mid-palatal suture. Children with maxillary contraction and dental malocclusion typically undergo such a procedure and have excellent results. However, OSAS is a multifactorial disorder; it does not seem related to the morphology of the oral cavity, and therefore, it is not always possible to cope with this problem exclusively through orthodontic treatment. Full article

(This article belongs to the Special Issue Moving beyond Current Diagnosis of Sleep-Disordered Breathing)

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