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Ding Q, Herrin J, Kryger M. Sex-specific associations between habitual snoring and cancer prevalence: insights from a US Cohort Study. SLEEP ADVANCES : A JOURNAL OF THE SLEEP RESEARCH SOCIETY 2024; 5:zpae051. [PMID: 39156215 PMCID: PMC11329803 DOI: 10.1093/sleepadvances/zpae051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 07/01/2024] [Indexed: 08/20/2024]
Abstract
Study Objectives To investigate the sex-specific association between habitual snoring and overall cancer prevalence and subtypes, and to examine the influence of age, body mass index (BMI), and sleep duration on this association. Methods This study utilized data from the National Health and Nutrition Examination Survey cycles between 2005 and 2020 and included 15 892 participants aged 18 and over. We employed inverse probability of treatment weighting based on propensity scores to adjust for confounders when comparing the prevalence of cancer between habitual snorers and non-habitual snorers for each sex and cancer type. Subgroup analyses were conducted based on sleep duration, age, and BMI categories. Results The cohort (mean age 48.2 years, 50.4% female, and 30.5% habitual snorers) reported 1385 cancer cases. In men, habitual snoring was linked to 26% lower odds of any cancer (OR 0.74, 95% CI: 0.66 to 0.83), while in women, it showed no significant difference except lower odds of breast cancer (OR 0.77, 95% CI: 0.63 to 0.94) and higher odds of cervix cancer (OR 1.54, 95% CI: 1.18 to 2.01). Age and sleep duration significantly influenced the snoring-cancer relationship, with notable variations by cancer type and sex. Conclusions Habitual snoring exhibits sex-specific associations with cancer prevalence, showing lower prevalence in men and varied results in women. These findings emphasize the critical need for further research to uncover the biological mechanisms involved. Future investigations should consider integrating sleep characteristics with cancer prevention and screening strategies, focusing on longitudinal research and the integration of genetic and biomarker analyses to fully understand these complex relationships.
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Affiliation(s)
- Qinglan Ding
- College of Health and Human Sciences, Purdue University, West Lafayette, IN, USA
| | - Jeph Herrin
- Division of Cardiology, Yale School of Medicine, New Haven, CT, USA
| | - Meir Kryger
- Division of Pulmonary, Critical Care & Sleep Medicine, Yale School of Medicine, New Haven, CT, USA
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Wang J, Ye Y, Chen X, Hu X, Peng Y. Sex Differences in the Relationship Between Self-Reporting of Snoring and Cardiovascular Risk:An Analysis of NHANES. Nat Sci Sleep 2024; 16:965-977. [PMID: 39050367 PMCID: PMC11268715 DOI: 10.2147/nss.s467516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Accepted: 07/09/2024] [Indexed: 07/27/2024] Open
Abstract
Background Identifying risk factors for cardiovascular disease (CVD) is critical for effective prevention and management. While classic CVD risk factors have been extensively studied, there is a scarcity of research on the association between snoring and CVD risk, particularly in the context of sex differences. Methods This study utilized data from the National Health and Nutrition Examination Survey (NHANES) conducted between 2015 and 2020. Participants were initially categorized based on the severity of snoring or the presence of snoring.Within the snoring group, they were further classified by sex. Analysis was carried out using multivariate logistic regression. Results Our study included 12,681 participants aged 18 years or older. When compared to the non-snoring group, individuals in the moderate snoring group had a higher odds ratio (OR) of 1.418 (95% CI 1.083 to 1.857, p = 0.011), while those in the severe snoring group had a higher OR of 1.882 (95% CI 1.468 to 2.409, p < 0.001). In the snoring group, individuals were further categorized by gender: 4527 males and 4131 females. Importantly, male patients showed a higher OR for atrial fibrillation (4.945, 95% CI 1.187 to 20.598, p = 0.028) compared to females. Additionally, male patients had a higher OR for coronary heart disease (2.002, 95% CI 1.152 to 3.479, p = 0.014) compared to females. Conclusion Sex plays a significant role in the relationship between snoring and CVD risk. Males with snoring have a higher risk of developing CVD compared to females. In particular, male snorers are nearly five times more likely to develop atrial fibrillation and about twice as likely to experience coronary artery disease in comparison to female snorers. It is recommended that healthcare providers and public health officials prioritize cardiovascular risk assessments for male individuals who exhibit symptoms of snoring.
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Affiliation(s)
- Junwen Wang
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China
| | - Yuyang Ye
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China
| | - Xuefeng Chen
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China
| | - Xinru Hu
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China
| | - Yong Peng
- Department of Cardiology, West China Hospital, Sichuan University, Chengdu, 610041, People’s Republic of China
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Leger D, Elbaz M. Diagnosing OSA and Insomnia at Home Based Only on an Actigraphy Total Sleep Time and RIP Belts an Algorithm "Nox Body Sleep™". Nat Sci Sleep 2024; 16:833-845. [PMID: 38911319 PMCID: PMC11194000 DOI: 10.2147/nss.s431650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 05/25/2024] [Indexed: 06/25/2024] Open
Abstract
Purpose The COVID-19 pandemic has influenced clinical sleep protocols with stricter hospital disinfection requirements. Facing these new rules, we tested if a new artificial intelligence (AI) algorithm: The Nox BodySleep™ (NBS) developed without airflow signals for the analysis of sleep might assess pertinently sleep in patients with Obstructive Sleep Apnea (OSA) and chronic insomnia (CI) as a control group, compared to polysomnography (PSG) manual scoring. Patients-Methods NBS is a recurrent neural network model that estimates Wake, NREM, and REM states, given features extracted from activity and respiratory inductance plethysmography (RIP) belt signals (Nox A1 PSG). Sleep states from 139 PSG studies (CI N = 72; OSA N = 67) were analyzed by NBS and compared to manually scored PSG using positive percentage agreement, negative percentage agreement, and overall agreement metrics. Similarly, we compared common sleep parameters and OSA severity using sleep states estimated by NBS for each recording and compared to manual scoring using Bland-Altman analysis and intra-class correlation coefficient. Results For 127,170 sleep epochs, an overall agreement of 83% was reached for Wake, NREM and REM states (92% for REM states in CI patients) between NBS and manually scored PSG. Overall agreement for estimating OSA severity was 100% for moderate-severe OSA and 91% for minimal OSA. The absolute errors of the apnea-hypopnea index (AHI) and total sleep time (TST) were significantly lower for the NBS compared to no scoring of sleep. The intra-class correlation was higher for AHI and significantly higher for TST using the NBS compared to no scoring of sleep. Conclusion NBS gives sleep states, parameters and AHI with a good positive and negative percentage agreement, compared with manually scored PSG.
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Affiliation(s)
- Damien Leger
- Université Paris Cité, (VIFASOM), ERC 7330 VIgilance FAtigue SOMmeil, Paris, France
- Assistance Publique-Hôpitaux de Paris (APHP) Hôtel Dieu, Centre du Sommeil et de la Vigilance, Paris, France
| | - Maxime Elbaz
- Université Paris Cité, (VIFASOM), ERC 7330 VIgilance FAtigue SOMmeil, Paris, France
- Assistance Publique-Hôpitaux de Paris (APHP) Hôtel Dieu, Centre du Sommeil et de la Vigilance, Paris, France
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4
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Shiao YH, Yu CC, Yeh YC. Validation of Downloadable Mobile Snore Applications by Polysomnography (PSG). Nat Sci Sleep 2024; 16:489-501. [PMID: 38800087 PMCID: PMC11127649 DOI: 10.2147/nss.s433351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 04/27/2024] [Indexed: 05/29/2024] Open
Abstract
Purpose Obstructive sleep apnea (OSA) is a common breathing disorder during sleep that is associated with symptoms such as snoring, excessive daytime sleepiness, and breathing interruptions. Polysomnography (PSG) is the most reliable diagnostic test for OSA; however, its high cost and lengthy testing duration make it difficult to access for many patients. With the availability of free snore applications for home-monitoring, this study aimed to validate the top three ranked snore applications, namely SnoreLab (SL), Anti Snore Solution (ASS), and Sleep Cycle Alarm (SCA), using PSG. Patients and Methods Sixty participants underwent an overnight PSG while simultaneously using three identical smartphones with the tested apps to gather sleep and snoring data. Results The study discovered that all three applications were significantly correlated with the total recording time and snore counts of PSG, with ASS showing good agreement with snore counts. Furthermore, the Snore Score, Time Snoring of SL, and Sleep Quality of SCA had a significant correlation with the natural logarithm of apnea hypopnea index (lnAHI) of PSG. The Snore Score of SL and the Sleep Quality of SCA were shown to be useful for evaluating snore severity and for pre-diagnosing or predicting OSA above moderate levels. Conclusion These findings suggest that some parameters of free snore applications can be employed to monitor OSA progress, and future research could involve adjusted algorithms and larger-scale studies to further authenticate these downloadable snore and sleep applications.
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Affiliation(s)
- Yi-Hsien Shiao
- Department of Traditional Chinese Medicine, Chang Gung Memorial Hospital, Keelung Medical Center, Keelung, Taiwan
- Graduate Institute of Natural Products, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Chung-Chieh Yu
- Department of Chest, Critical Care, and Sleep Medicine, Chang Gung Memorial Hospital, Keelung Medical Center, Keelung, Taiwan
| | - Yuan-Chieh Yeh
- Department of Traditional Chinese Medicine, Chang Gung Memorial Hospital, Keelung Medical Center, Keelung, Taiwan
- Program in Molecular Medicine, College of Life Sciences, National Yang Ming Chiao Tung University, Taipei, Taiwan
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Cao S, Rosenzweig I, Bilotta F, Jiang H, Xia M. Automatic detection of obstructive sleep apnea based on speech or snoring sounds: a narrative review. J Thorac Dis 2024; 16:2654-2667. [PMID: 38738242 PMCID: PMC11087644 DOI: 10.21037/jtd-24-310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 04/15/2024] [Indexed: 05/14/2024]
Abstract
Background and Objective Obstructive sleep apnea (OSA) is a common chronic disorder characterized by repeated breathing pauses during sleep caused by upper airway narrowing or collapse. The gold standard for OSA diagnosis is the polysomnography test, which is time consuming, expensive, and invasive. In recent years, more cost-effective approaches for OSA detection based in predictive value of speech and snoring has emerged. In this paper, we offer a comprehensive summary of current research progress on the applications of speech or snoring sounds for the automatic detection of OSA and discuss the key challenges that need to be overcome for future research into this novel approach. Methods PubMed, IEEE Xplore, and Web of Science databases were searched with related keywords. Literature published between 1989 and 2022 examining the potential of using speech or snoring sounds for automated OSA detection was reviewed. Key Content and Findings Speech and snoring sounds contain a large amount of information about OSA, and they have been extensively studied in the automatic screening of OSA. By importing features extracted from speech and snoring sounds into artificial intelligence models, clinicians can automatically screen for OSA. Features such as formant, linear prediction cepstral coefficients, mel-frequency cepstral coefficients, and artificial intelligence algorithms including support vector machines, Gaussian mixture model, and hidden Markov models have been extensively studied for the detection of OSA. Conclusions Due to the significant advantages of noninvasive, low-cost, and contactless data collection, an automatic approach based on speech or snoring sounds seems to be a promising tool for the detection of OSA.
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Affiliation(s)
- Shuang Cao
- Department of Anesthesiology, The Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ivana Rosenzweig
- Sleep and Brain Plasticity Centre, CNS, IoPPN, King’s College London, London, UK
- Sleep Disorders Centre, Guy’s and St Thomas’ Hospital, GSTT NHS, London, UK
| | - Federico Bilotta
- Department of Anaesthesia and Critical Care Medicine, Policlinico Umberto 1 Hospital, Sapienza University of Rome, Rome, Italy
| | - Hong Jiang
- Department of Anesthesiology, The Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ming Xia
- Department of Anesthesiology, The Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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Sun J, Dong QX, Wang SW, Zheng YB, Liu XX, Lu TS, Yuan K, Shi J, Hu B, Lu L, Han Y. Artificial intelligence in psychiatry research, diagnosis, and therapy. Asian J Psychiatr 2023; 87:103705. [PMID: 37506575 DOI: 10.1016/j.ajp.2023.103705] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 07/16/2023] [Accepted: 07/20/2023] [Indexed: 07/30/2023]
Abstract
Psychiatric disorders are now responsible for the largest proportion of the global burden of disease, and even more challenges have been seen during the COVID-19 pandemic. Artificial intelligence (AI) is commonly used to facilitate the early detection of disease, understand disease progression, and discover new treatments in the fields of both physical and mental health. The present review provides a broad overview of AI methodology and its applications in data acquisition and processing, feature extraction and characterization, psychiatric disorder classification, potential biomarker detection, real-time monitoring, and interventions in psychiatric disorders. We also comprehensively summarize AI applications with regard to the early warning, diagnosis, prognosis, and treatment of specific psychiatric disorders, including depression, schizophrenia, autism spectrum disorder, attention-deficit/hyperactivity disorder, addiction, sleep disorders, and Alzheimer's disease. The advantages and disadvantages of AI in psychiatry are clarified. We foresee a new wave of research opportunities to facilitate and improve AI technology and its long-term implications in psychiatry during and after the COVID-19 era.
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Affiliation(s)
- Jie Sun
- Pain Medicine Center, Peking University Third Hospital, Beijing 100191, China; Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Qun-Xi Dong
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China
| | - San-Wang Wang
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China
| | - Yong-Bo Zheng
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Xiao-Xing Liu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Tang-Sheng Lu
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Kai Yuan
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China
| | - Jie Shi
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China
| | - Bin Hu
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, China.
| | - Lin Lu
- Peking University Sixth Hospital, Peking University Institute of Mental Health, NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing 100191, China; Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China.
| | - Ying Han
- National Institute on Drug Dependence and Beijing Key Laboratory of Drug Dependence Research, Peking University, Beijing 100191, China.
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Sheta A, Thaher T, Surani SR, Turabieh H, Braik M, Too J, Abu-El-Rub N, Mafarjah M, Chantar H, Subramanian S. Diagnosis of Obstructive Sleep Apnea Using Feature Selection, Classification Methods, and Data Grouping Based Age, Sex, and Race. Diagnostics (Basel) 2023; 13:2417. [PMID: 37510161 PMCID: PMC10377846 DOI: 10.3390/diagnostics13142417] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 07/13/2023] [Accepted: 07/15/2023] [Indexed: 07/30/2023] Open
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, 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.
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Affiliation(s)
- Alaa Sheta
- Computer Science Department, Southern Connecticut State University, New Haven, CT 06514, USA
| | - Thaer Thaher
- Department of Computer Systems Engineering, Arab American University, Jenin P.O. Box 240, Palestine
| | - Salim R Surani
- Department of Pulmonary, Critical Care & Sleep Medicine, Texas A&M University, College Station, TX 77843, USA
| | - Hamza Turabieh
- Health Management and Informatics Department, School of Medicine, University of Missouri, Columbia, MO 65212, USA
| | - Malik Braik
- Department of Computer Science, Al-Balqa Applied University, Salt 19117, Jordan
| | - Jingwei Too
- Faculty of Electrical Engineering, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, Durian Tunggal 76100, Melaka, Malaysia
| | - Noor Abu-El-Rub
- Center of Medical Informatics and Enterprise Analytics, University of Kansas Medical Center, Kansas City, KS 66160, USA
| | - Majdi Mafarjah
- Department of Computer Science, Birzeit University, Birzeit P.O. Box 14, Palestine
| | - Hamouda Chantar
- Faculty of Information Technology, Sebha University, Sebha 18758, Libya
| | - Shyam Subramanian
- Pulmonary, Critical Care & Sleep Medicine, Sutter Health, Tracy, CA 95376, USA
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Bazoukis G, Bollepalli SC, Chung CT, Li X, Tse G, Bartley BL, Batool-Anwar S, Quan SF, Armoundas AA. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med 2023; 19:1337-1363. [PMID: 36856067 PMCID: PMC10315608 DOI: 10.5664/jcsm.10532] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 02/21/2023] [Accepted: 02/21/2023] [Indexed: 03/02/2023]
Abstract
STUDY OBJECTIVES Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders. METHODS A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed. RESULTS Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models. CONCLUSIONS The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently. CITATION Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.
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Affiliation(s)
- George Bazoukis
- Department of Cardiology, Larnaca General Hospital, Larnaca, Cyprus
- Department of Basic and Clinical Sciences, University of Nicosia Medical School, Nicosia, Cyprus
| | | | - Cheuk To Chung
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
| | - Xinmu Li
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease, Department of Cardiology, Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University, Tianjin, China
| | - Gary Tse
- Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, China-UK Collaboration, Hong Kong
- Kent and Medway Medical School, Canterbury, Kent, United Kingdom
| | - Bethany L. Bartley
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Salma Batool-Anwar
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
| | - Stuart F. Quan
- Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston, Massachusetts
- Asthma and Airway Disease Research Center, University of Arizona College of Medicine, Tucson, Arizona
| | - Antonis A. Armoundas
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts
- Broad Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts
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Hemrajani P, Dhaka VS, Rani G, Shukla P, Bavirisetti DP. Efficient Deep Learning Based Hybrid Model to Detect Obstructive Sleep Apnea. SENSORS (BASEL, SWITZERLAND) 2023; 23:4692. [PMID: 37430605 DOI: 10.3390/s23104692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/23/2023] [Accepted: 04/25/2023] [Indexed: 07/12/2023]
Abstract
An increasing number of patients and a lack of awareness about obstructive sleep apnea is a point of concern for the healthcare industry. Polysomnography is recommended by health experts to detect obstructive sleep apnea. The patient is paired up with devices that track patterns and activities during their sleep. Polysomnography, being a complex and expensive process, cannot be adopted by the majority of patients. Therefore, an alternative is required. The researchers devised various machine learning algorithms using single lead signals such as electrocardiogram, oxygen saturation, etc., for the detection of obstructive sleep apnea. These methods have low accuracy, less reliability, and high computation time. Thus, the authors introduced two different paradigms for the detection of obstructive sleep apnea. The first is MobileNet V1, and the other is the convergence of MobileNet V1 with two separate recurrent neural networks, Long-Short Term Memory and Gated Recurrent Unit. They evaluate the efficacy of their proposed method using authentic medical cases from the PhysioNet Apnea-Electrocardiogram database. The model MobileNet V1 achieves an accuracy of 89.5%, a convergence of MobileNet V1 with LSTM achieves an accuracy of 90%, and a convergence of MobileNet V1 with GRU achieves an accuracy of 90.29%. The obtained results prove the supremacy of the proposed approach in comparison to the state-of-the-art methods. To showcase the implementation of devised methods in a real-life scenario, the authors design a wearable device that monitors ECG signals and classifies them into apnea and normal. The device employs a security mechanism to transmit the ECG signals securely over the cloud with the consent of patients.
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Affiliation(s)
- Prashant Hemrajani
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
| | - Vijaypal Singh Dhaka
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
| | - Geeta Rani
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
| | - Praveen Shukla
- Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, Rajasthan, India
| | - Durga Prasad Bavirisetti
- Department of Computer Science, Norwegian University of Science and Technology, 7034 Trondheim, Norway
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Lei L, Wang Y, Zhao F, Jiang Z, Zhao Y, Yu L, Zhu P, Zou J. Behavioral and cognitive outcomes of habitual snoring in children aged 2-14 years in Chengdu, Sichuan. Am J Otolaryngol 2023; 44:103691. [PMID: 36669275 DOI: 10.1016/j.amjoto.2022.103691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 09/18/2022] [Accepted: 11/11/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE Habitual snoring is associated with cognitive, behavioral, and other physiological problems of children. Few studies have reported specifically on the relationships between snoring and those problems in children as noticed by their parents. We aimed to identify the cognitive, behavioral, and sleep-related nocturnal problems in children with HS as noted by their parents. MATERIALS AND METHODS A cross-sectional survey was performed in Chengdu, Sichuan, China. Children aged 2-14 years from four districts were randomly chosen to participate. Questionnaires were completed voluntarily by the children's parents/guardians. RESULTS A total of 1548 questionnaires were analyzed and classified those children as 463 habitual snorers (HS group, 30.4 %), 683 occasional snorers (OS group, 44.8 %), and 402 non-snorers (NS group, 26.4 %). The percentages of children with sleep-related nocturnal symptoms were 94.6 %, 87.3 %, and 66.9 % in the HS, OS, and NS groups. Percentages of children with cognitive problems were 76.2 %, 74.6 %, and 64.9 % in the HS, OS, and NS groups, respectively (P < 0.001). The frequencies of daytime behavioral problems were 68.3 %, 61.5 %, and 46.8%in the HS, OS, and NS groups, respectively (P < 0.001).The average number of sleep-related nocturnal symptoms, cognitive symptoms and daytime behavioral problems was higher in the HS group than in the OS and NS groups. CONCLUSIONS HS is a significant contributor to sleep-related nocturnal symptoms and daytime cognitive and behavioral problems in children, as reported by their parents/guardians. HS and OS are important contributors to poor sleep quality and daytime cognitive and behavioral problems in children.
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Affiliation(s)
- Lei Lei
- Department of Otorhinolaryngology, Head & Neck Surgery, West China Hospital, West China Medical School, Sichuan University, Sichuan, China
| | - Yifei Wang
- West China Medical School, Sichuan University, Sichuan, China
| | - Fanyu Zhao
- West China Medical School, Sichuan University, Sichuan, China
| | - Zijing Jiang
- West China Medical School, Sichuan University, Sichuan, China
| | - Yu Zhao
- Department of Otorhinolaryngology, Head & Neck Surgery, West China Hospital, West China Medical School, Sichuan University, Sichuan, China
| | - Lingyu Yu
- Department of Otorhinolaryngology, Head & Neck Surgery, West China Hospital, West China Medical School, Sichuan University, Sichuan, China
| | - Ping Zhu
- Department of Clinical Research Management, West China Hospital, Sichuan University, Sichuan, China.
| | - Jian Zou
- Department of Otorhinolaryngology, Head & Neck Surgery, West China Hospital, West China Medical School, Sichuan University, Sichuan, China.
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11
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Huang Z, Lobbezoo F, Vanhommerig JW, Volgenant CMC, de Vries N, Aarab G, Hilgevoord AAJ. Effects of demographic and sleep-related factors on snoring sound parameters. Sleep Med 2023; 104:3-10. [PMID: 36857868 DOI: 10.1016/j.sleep.2023.02.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 02/25/2023]
Abstract
OBJECTIVE To investigate the effect of frequently reported between-individual (viz., age, gender, body mass index [BMI], and apnea-hypopnea index [AHI]) and within-individual (viz., sleep stage and sleep position) snoring sound-related factors on snoring sound parameters in temporal, intensity, and frequency domains. METHODS This study included 83 adult snorers (mean ± SD age: 42.2 ± 11.3 yrs; male gender: 59%) who underwent an overnight polysomnography (PSG) and simultaneous sound recording, from which a total of 131,745 snoring events were extracted and analyzed. Data on both between-individual and within-individual factors were extracted from the participants' PSG reports. RESULTS Gender did not have any significant effect on snoring sound parameters. The fundamental frequency (FF; coefficient = -0.31; P = 0.02) and dominant frequency (DF; coefficient = -12.43; P < 0.01) of snoring sounds decreased with the increase of age, and the second formant increased (coefficient = 22.91; P = 0.02) with the increase of BMI. Severe obstructive sleep apnea (OSA; AHI ≥30 events/hour), non-rapid eye movement sleep stage 3 (N3), and supine position were all associated with more, longer, and louder snoring events (P < 0.05). Supine position was associated with higher FF and DF, and lateral decubitus positions were associated with higher formants. CONCLUSIONS Within the limitations of the current patient profile and included factors, AHI was found to have greater effects on snoring sound parameters than the other between-individual factors. The included within-individual factors were found to have greater effects on snoring sound parameters than the between-individual factors under study.
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Affiliation(s)
- Zhengfei Huang
- Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Clinical Neurophysiology, OLVG, Amsterdam, the Netherlands.
| | - Frank Lobbezoo
- Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Joost W Vanhommerig
- Department of Research and Epidemiology, OLVG Hospital, Amsterdam, the Netherlands
| | - Catherine M C Volgenant
- Department of Preventive Dentistry, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Nico de Vries
- Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands; Department of Otorhinolaryngology - Head and Neck Surgery, OLVG, Amsterdam, the Netherlands; Department of Otorhinolaryngology - Head and Neck Surgery, Antwerp University Hospital (UZA), Antwerp, Belgium
| | - Ghizlane Aarab
- Department of Orofacial Pain and Dysfunction, Academic Center for Dentistry Amsterdam (ACTA), University of Amsterdam and Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
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12
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Kerkamm F, Dengler D, Eichler M, Materzok-Köppen D, Belz L, Neumann FA, Zyriax BC, Harth V, Oldenburg M. Sleep Architecture and Sleep-Related Breathing Disorders of Seafarers on Board Merchant Ships: A Polysomnographic Pilot Field Study on the High Seas. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:3168. [PMID: 36833863 PMCID: PMC9962439 DOI: 10.3390/ijerph20043168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 02/02/2023] [Accepted: 02/08/2023] [Indexed: 06/18/2023]
Abstract
As seafarers are assumed to have an increased risk profile for sleep-related breathing disorders, this cross-sectional observational study measured (a) the feasibility and quality of polysomnography (PSG) on board merchant ships, (b) sleep macro- and microarchitecture, (c) sleep-related breathing disorders, such as obstructive sleep apnea (OSA), using the apnea-hypopnea index (AHI), and (d) subjective and objective sleepiness using the Epworth Sleepiness Scale (ESS) and pupillometry. Measurements were carried out on two container ships and a bulk carrier. A total of 19 out of 73 male seafarers participated. The PSG's signal qualities and impedances were comparable to those in a sleep laboratory without unusual artifacts. Compared to the normal population, seafarers had a lower total sleep time, a shift of deep sleep phases in favor of light sleep phases as well as an increased arousal index. Additionally, 73.7% of the seafarers were diagnosed with at least mild OSA (AHI ≥ 5) and 15.8% with severe OSA (AHI ≥ 30). In general, seafarers slept in the supine position with a remarkable frequency of breathing cessations. A total of 61.1% of the seafarers had increased subjective daytime sleepiness (ESS > 5). Pupillometry results for objective sleepiness revealed a mean relative pupillary unrest index (rPUI) of 1.2 (SD 0.7) in both occupational groups. In addition, significantly poorer objective sleep quality was found among the watchkeepers. A need for action with regard to poor sleep quality and daytime sleepiness of seafarers on board is indicated. A slightly increased prevalence of OSA among seafarers is likely.
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Affiliation(s)
- Fiona Kerkamm
- Institute for Occupational and Maritime Medicine Hamburg (ZfAM), University Medical Center Hamburg-Eppendorf (UKE), 20459 Hamburg, Germany
| | - Dorothee Dengler
- Institute for Occupational and Maritime Medicine Hamburg (ZfAM), University Medical Center Hamburg-Eppendorf (UKE), 20459 Hamburg, Germany
| | - Matthias Eichler
- Institute for Occupational and Maritime Medicine Hamburg (ZfAM), University Medical Center Hamburg-Eppendorf (UKE), 20459 Hamburg, Germany
| | - Danuta Materzok-Köppen
- Institute for Occupational and Maritime Medicine Hamburg (ZfAM), University Medical Center Hamburg-Eppendorf (UKE), 20459 Hamburg, Germany
| | - Lukas Belz
- Institute for Occupational and Maritime Medicine Hamburg (ZfAM), University Medical Center Hamburg-Eppendorf (UKE), 20459 Hamburg, Germany
| | - Felix Alexander Neumann
- Preventive Medicine and Nutrition, Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), 20459 Hamburg, Germany
| | - Birgit-Christiane Zyriax
- Preventive Medicine and Nutrition, Institute for Health Services Research in Dermatology and Nursing (IVDP), University Medical Center Hamburg-Eppendorf (UKE), 20459 Hamburg, Germany
| | - Volker Harth
- Institute for Occupational and Maritime Medicine Hamburg (ZfAM), University Medical Center Hamburg-Eppendorf (UKE), 20459 Hamburg, Germany
| | - Marcus Oldenburg
- Institute for Occupational and Maritime Medicine Hamburg (ZfAM), University Medical Center Hamburg-Eppendorf (UKE), 20459 Hamburg, Germany
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13
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Tsai CY, Liu WT, Hsu WH, Majumdar A, Stettler M, Lee KY, Cheng WH, Wu D, Lee HC, Kuan YC, Wu CJ, Lin YC, Ho SC. Screening the risk of obstructive sleep apnea by utilizing supervised learning techniques based on anthropometric features and snoring events. Digit Health 2023; 9:20552076231152751. [PMID: 36896329 PMCID: PMC9989412 DOI: 10.1177/20552076231152751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 01/04/2023] [Indexed: 03/08/2023] Open
Abstract
Objectives Obstructive sleep apnea (OSA) is typically diagnosed by polysomnography (PSG). However, PSG is time-consuming and has some clinical limitations. This study thus aimed to establish machine learning models to screen for the risk of having moderate-to-severe and severe OSA based on easily acquired features. Methods We collected PSG data on 3529 patients from Taiwan and further derived the number of snoring events. Their baseline characteristics and anthropometric measures were obtained, and correlations among the collected variables were investigated. Next, six common supervised machine learning techniques were utilized, including random forest (RF), extreme gradient boosting (XGBoost), k-nearest neighbor (kNN), support vector machine (SVM), logistic regression (LR), and naïve Bayes (NB). First, data were independently separated into a training and validation dataset (80%) and a test dataset (20%). The approach with the highest accuracy in the training and validation phase was employed to classify the test dataset. Next, feature importance was investigated by calculating the Shapley value of every factor, which represented the impact on OSA risk screening. Results The RF produced the highest accuracy (of >70%) in the training and validation phase in screening for both OSA severities. Hence, we employed the RF to classify the test dataset, and results showed a 79.32% accuracy for moderate-to-severe OSA and 74.37% accuracy for severe OSA. Snoring events and the visceral fat level were the most and second most essential features of screening for OSA risk. Conclusions The established model can be considered for screening for the risk of having moderate-to-severe or severe OSA.
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Affiliation(s)
- Cheng-Yu Tsai
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Wen-Te Liu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Research Center of Artificial Intelligence in Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wen-Hua Hsu
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Arnab Majumdar
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Marc Stettler
- Department of Civil and Environmental Engineering, Imperial College London, London, UK
| | - Kang-Yun Lee
- Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Wun-Hao Cheng
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Dean Wu
- Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan.,Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Hsin-Chien Lee
- Department of Psychiatry, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yi-Chun Kuan
- Sleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan.,Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Cheng-Jung Wu
- Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Yi-Chih Lin
- Department of Otolaryngology, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Shu-Chuan Ho
- School of Respiratory Therapy, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
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14
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Chiang JK, Lin YC, Lu CM, Kao YH. Snoring Index and Neck Circumference as Predictors of Adult Obstructive Sleep Apnea. Healthcare (Basel) 2022; 10:healthcare10122543. [PMID: 36554066 PMCID: PMC9778532 DOI: 10.3390/healthcare10122543] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 12/12/2022] [Accepted: 12/13/2022] [Indexed: 12/23/2022] Open
Abstract
Background. Snoring is the cardinal symptom of obstructive sleep apnea (OSA). The acoustic features of snoring sounds include intra-snore (including snoring index [SI]) and inter-snore features. However, the correlation between snoring sounds and the severity of OSA according to the apnea−hypopnea index (AHI) is still unclear. We aimed to use the snoring index (SI) and the Epworth Sleepiness Scale (ESS) to predict OSA and its severity according to the AHI among middle-aged participants referred for polysomnography (PSG). Methods. In total, 50 participants (mean age, 47.5 ± 12.6 years; BMI: 29.2 ± 5.6 kg/m2) who reported snoring and were referred for a diagnosis of OSA and who underwent a whole night of PSG were recruited. Results. The mean AHI was 30.2 ± 27.2, and the mean SI was 87.9 ± 56.3 events/hour. Overall, 11 participants had daytime sleepiness (ESS > 10). The correlation between SI and AHI (r = 0.33, p = 0.021) was significant. Univariate linear regression analysis showed that male gender, body mass index, neck circumference, ESS, and SI were associated with AHI. SI (β = 0.18, p = 0.004) and neck circumference (β = 2.40, p < 0.001) remained significantly associated with AHI by the multivariate linear regression model. Conclusion. The total number of snores per hour of sleep and neck circumference were positively associated with OSA among adults referred for PSG.
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Affiliation(s)
- Jui-Kun Chiang
- Department of Family Medicine, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi 622, Taiwan
| | | | - Chih-Ming Lu
- Department of Urology, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Chiayi 622, Taiwan
| | - Yee-Hsin Kao
- Department of Family Medicine, Tainan Municipal Hospital (Managed by Show Chwan Medical Care Corporation), Tainan 701, Taiwan
- Correspondence: ; Tel.: +886-6-2609926 (ext. 23104)
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15
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Röthenbacher A, Cesari M, Doppler CEJ, Okkels N, Willemsen N, Sembowski N, Seger A, Lindner M, Brune C, Stefani A, Högl B, Bialonski S, Borghammer P, Fink GR, Schober M, Sommerauer M. RBDtector: an open-source software to detect REM sleep without atonia according to visual scoring criteria. Sci Rep 2022; 12:20886. [PMID: 36463304 PMCID: PMC9719467 DOI: 10.1038/s41598-022-25163-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 11/25/2022] [Indexed: 12/07/2022] Open
Abstract
REM sleep without atonia (RSWA) is a key feature for the diagnosis of rapid eye movement (REM) sleep behaviour disorder (RBD). We introduce RBDtector, a novel open-source software to score RSWA according to established SINBAR visual scoring criteria. We assessed muscle activity of the mentalis, flexor digitorum superficialis (FDS), and anterior tibialis (AT) muscles. RSWA was scored manually as tonic, phasic, and any activity by human scorers as well as using RBDtector in 20 subjects. Subsequently, 174 subjects (72 without RBD and 102 with RBD) were analysed with RBDtector to show the algorithm's applicability. We additionally compared RBDtector estimates to a previously published dataset. RBDtector showed robust conformity with human scorings. The highest congruency was achieved for phasic and any activity of the FDS. Combining mentalis any and FDS any, RBDtector identified RBD subjects with 100% specificity and 96% sensitivity applying a cut-off of 20.6%. Comparable performance was obtained without manual artefact removal. RBD subjects also showed muscle bouts of higher amplitude and longer duration. RBDtector provides estimates of tonic, phasic, and any activity comparable to human scorings. RBDtector, which is freely available, can help identify RBD subjects and provides reliable RSWA metrics.
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Affiliation(s)
- Annika Röthenbacher
- grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Matteo Cesari
- grid.5361.10000 0000 8853 2677Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Christopher E. J. Doppler
- grid.411097.a0000 0000 8852 305XDepartment of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Leo-Brandt-Str. 5, 52425 Jülich, Germany
| | - Niels Okkels
- grid.154185.c0000 0004 0512 597XDepartment of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark ,grid.154185.c0000 0004 0512 597XDepartment of Neurology, Aarhus University Hospital, Aarhus, Denmark ,grid.7048.b0000 0001 1956 2722Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Nele Willemsen
- grid.411097.a0000 0000 8852 305XDepartment of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nora Sembowski
- grid.411097.a0000 0000 8852 305XDepartment of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Aline Seger
- grid.411097.a0000 0000 8852 305XDepartment of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Leo-Brandt-Str. 5, 52425 Jülich, Germany
| | - Marie Lindner
- grid.411097.a0000 0000 8852 305XDepartment of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Corinna Brune
- grid.411097.a0000 0000 8852 305XDepartment of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Ambra Stefani
- grid.5361.10000 0000 8853 2677Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Birgit Högl
- grid.5361.10000 0000 8853 2677Department of Neurology, Medical University of Innsbruck, Innsbruck, Austria
| | - Stephan Bialonski
- grid.434081.a0000 0001 0698 0538Department of Medical Engineering and Technomathematics, FH Aachen University of Applied Sciences, Jülich, Germany ,grid.434081.a0000 0001 0698 0538Institute for Data-Driven Technologies, FH Aachen University of Applied Sciences, Jülich, Germany
| | - Per Borghammer
- grid.154185.c0000 0004 0512 597XDepartment of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
| | - Gereon R. Fink
- grid.411097.a0000 0000 8852 305XDepartment of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Leo-Brandt-Str. 5, 52425 Jülich, Germany
| | - Martin Schober
- grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Michael Sommerauer
- grid.411097.a0000 0000 8852 305XDepartment of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany ,grid.8385.60000 0001 2297 375XInstitute of Neuroscience and Medicine (INM-3), Forschungszentrum Jülich, Leo-Brandt-Str. 5, 52425 Jülich, Germany ,grid.154185.c0000 0004 0512 597XDepartment of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
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16
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Chiang JK, Lin YC, Lu CM, Kao YH. Correlation between snoring sounds and obstructive sleep apnea in adults: a meta-regression analysis. Sleep Sci 2022; 15:463-470. [PMID: 36419807 PMCID: PMC9670768 DOI: 10.5935/1984-0063.20220068] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 02/07/2022] [Indexed: 09/17/2023] Open
Abstract
OBJECTIVE Snoring is a dominant clinical symptom in patients with obstructive sleep apnea (OSA), and analyzing snoring sounds might be a potential alternative to polysomnography (PSG) for the assessment of OSA. This study aimed to systematically examine the correlation between the snoring sounds and the apnea-hypopnea index (AHI) as the measures of OSA severity. MATERIAL AND METHODS A comprehensive literature review using the MEDLINE, Embase, Cochrane Library, Scopus, and PubMed databases identified the published studies reporting the correlations between and severity of snoring and the AHI values by meta-regression analysis. RESULTS In total, 13 studies involving 3,153 adult patients were included in this study. The pooled correlation coefficient for snoring sounds and AHI values was 0.71 (95%CI: 0.49, 0.85) from the random-effects meta-analysis with the Knapp and Hartung adjustment. The I 2 and chi-square Q test demonstrated significant heterogeneity (97.6% and p<0.001). After adjusting for the effects of the other covariates, the mean value of the Fisher's r-to-z transformed correlation coefficient would have 0.80 less by the snoring rate (95%CI = -1.02, -0.57), 1.46 less by the snoring index (95%CI = -1.85, -1.07), and 0.21 less in the mean body mass index (95%CI = -0.31, -0.11), but 0.15 more in the mean age (95%CI = 0.10, 0.20). It fitted the data very well (R 2=0.9641). CONCLUSION A high correlation between the severity of snoring and the AHI was found in the studies with PSG. As compared to the snoring rate and the snoring index, the snoring intensity, the snoring frequency, and the snoring time interval index were more sensitive measures for the severity of snoring.
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Affiliation(s)
- Jui-Kun Chiang
- Dalin Tzu Chi Hospital, Family Medicine - Chiayi - Taiwan
| | - Yen-Chang Lin
- Nature Dental Clinic, Dental department - Puli - Taiwan
| | - Chih-Ming Lu
- Dalin Tzu Chi Hospital, Department of Urology - Chiayi - Taiwan
| | - Yee-Hsin Kao
- Tainan Municipal Hospital (Managed by Show Chwan Medical Care
Corporation), Family Medicine - Tainan - Taiwan
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17
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Early detection of obstructive sleep apnea in patients with atrial fibrillation. J Am Assoc Nurse Pract 2022; 34:1083-1089. [PMID: 36083320 DOI: 10.1097/jxx.0000000000000766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Accepted: 07/06/2022] [Indexed: 11/26/2022]
Abstract
BACKGROUND Obstructive sleep apnea (OSA) is an independent and modifiable risk factor for atrial fibrillation (AF) and correlates with a three-fold higher risk of incident AF. Although OSA is prevalent in patients with AF, it remains underdiagnosed. Guidelines for OSA screening are ambiguous. LOCAL PROBLEM A small community hospital in the southeast United States lacked standardized OSA screening and consistent sleep clinic referral for hospitalized patients with AF. METHODS Over 3 months, an OSA bundle (including screening, education, and referral) was implemented for hospitalized patients with AF. A retrospective electronic health record (EHR) review established a baseline comparison group. Descriptive analyses between the intervention and comparison groups evaluated the effectiveness of the OSA bundle. INTERVENTIONS Eligible patients received OSA screening with the STOP-Bang questionnaire. A STOP-Bang score of 3 or higher triggered patient education about the arrhythmogenic relationship of OSA and AF. At discharge, patients received an ambulatory sleep clinic referral. After 3 months, an EHR review assessed the rate of sleep clinic follow-up, sleep testing, OSA diagnosis, and initiation of positive airway pressure. RESULTS Of the 68 patients in the comparison group and 33 patients in the intervention group, the rate of OSA screening increased from 4.4% to 100%. Sleep clinic referral increased from 66.7% to 93.5%. Sleep clinic follow-up increased from 0% to 10%. CONCLUSION Screening for OSA and sleep clinic referral improved with the OSA bundle; however, sleep clinic follow-up remained low. Further quantitative and qualitative investigation is needed to better understand barriers to sleep clinic follow-up.
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18
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Kok XH, Imtiaz SA, Rodriguez-Villegas E. Automatic Identification of Snoring and Groaning Segments in Acoustic Recordings. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1993-1996. [PMID: 36086260 DOI: 10.1109/embc48229.2022.9871863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Sleep-related breathing disorders have severe impact on the quality of lives of those suffering from them. These disorders present with a variety of symptoms, out of which snoring and groaning are very common. This paper presents an algorithm to identify and classify segments of acoustic respiratory sound recordings that contain both groaning and snoring events. The recordings were obtained from a database containing 20 subjects from which features based on the Mel-frequency cepstral coefficients (MFCC) were extracted. In the first stage of the algorithm, segments of recordings consisting of either snoring or groaning episodes - without classifying them - were identified. In the second stage, these segments were further differentiated into individual groaning or snoring events. The algorithm in the first stage achieved a sensitivity and specificity of 90.5% ±2.9% and 90.0% ±1.6% respectively, using a RUSBoost model. In the second stage, a random forest classifier was used, and the accuracies for groan and snore events were 78.1% ±4.7% and 78.4% ±4.7% respectively.
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19
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Borsky M, Serwatko M, Arnardottir ES, Mallett J. Towards Sleep Study Automation: Detection Evaluation of Respiratory-Related Events. IEEE J Biomed Health Inform 2022; 26:3418-3426. [PMID: 35294367 DOI: 10.1109/jbhi.2022.3159727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The diagnosis of sleep disordered breathing depends on the detection of several respiratory-related events: apneas, hypopneas, snores, or respiratory event-related arousals from sleep studies. While a number of automatic detection methods have been proposed, reproducibility of these methods has been an issue, in part due to the absence of a generally accepted protocol for evaluating their results. With sleep measurements this is usually treated as a classification problem and the accompanying issue of localization is not treated as similarly critical. To address these problems we present a detection evaluation protocol that is able to qualitatively assess the match between two annotations of respiratory-related events. This protocol relies on measuring the relative temporal overlap between two annotations in order to find an alignment that maximizes their F1-score at the sequence level. This protocol can be used in applications which require a precise estimate of the number of events, total event duration, and a joint estimate of event number and duration. We assess its application using a data set that contains over 10,000 manually annotated snore events from 9 subjects, and show that when using the American Academy of Sleep Medicine Manual standard, two sleep technologists can achieve an F1-score of 0.88 when identifying the presence of snore events. In addition, we drafted rules for marking snore boundaries and showed that one sleep technologist can achieve F1-score of 0.94 at the same tasks. Finally, we compared our protocol against the protocol that is used to evaluate sleep spindle detection and highlighted the differences.
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20
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Fan H, Liu Z, Zhang X, Yuan H, Zhao X, Zhao R, Shi T, Wu S, Xu Y, Suo C, Chen X, Zhang T. Investigating the Association Between Seven Sleep Traits and Nonalcoholic Fatty Liver Disease: Observational and Mendelian Randomization Study. Front Genet 2022; 13:792558. [PMID: 35656325 PMCID: PMC9152285 DOI: 10.3389/fgene.2022.792558] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Background and Aim: Aberrant sleep parameters are associated with the risk of nonalcoholic fatty liver disease (NAFLD). However, existing information is inconsistent among studies and involves reverse causation. Therefore, we aimed to investigate the observational associations and causations between sleep traits and NAFLD. Methods: We performed multivariable regression to assess observational associations of seven sleep traits (sleep duration, easiness of getting up in the morning, chronotype, nap during day, snoring, insomnia, and narcolepsy), and NAFLD in the UK Biobank (1,029 NAFLD). The Cox proportional hazards model was applied to derive hazard ratios and 95% confidence intervals (CIs). Furthermore, a bidirectional two-sample Mendelian randomization (MR) approach was used to explore the causal relationships between sleep traits and NAFLD. Results: In the multivariable regression model adjusted for potential confounders, getting up in the morning not at all easy (HR, 1.51; 95% CI, 1.27-1.78) and usually insomnia (HR, 1.46; 95% CI, 1.21-1.75) were associated with the risk of NAFLD. Furthermore, the easiness of getting up in the morning and insomnia showed a dose-response association with NAFLD (Ptrend <0.05). MR analysis found consistent causal effects of NAFLD on easiness of getting up in the morning (OR, 0.995; 95% CI, 0.990-0.999; p = 0.033) and insomnia (OR, 1.006; 95% CI, 1.001-1.011; p = 0.024). These results were robust to weak instrument bias, pleiotropy, and heterogeneity. Conclusions: Findings showed consistent evidence of observational analyses and MR analyses that trouble getting up in the morning and insomnia were associated with an increased risk of NAFLD. Bidirectional MR demonstrated causal effects of NAFLD on sleep traits.
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Affiliation(s)
- Hong Fan
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.,Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China.,Fudan University Taizhou Institute of Health Sciences, Taizhou, China
| | - Zhenqiu Liu
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.,Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China.,Fudan University Taizhou Institute of Health Sciences, Taizhou, China.,State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China
| | - Xin Zhang
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.,Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China.,Fudan University Taizhou Institute of Health Sciences, Taizhou, China
| | - Huangbo Yuan
- Fudan University Taizhou Institute of Health Sciences, Taizhou, China
| | - Xiaolan Zhao
- Department of Chronic Diseases Prevention, Taizhou Center for Disease Control and Prevention, Jiangsu, China
| | - Renjia Zhao
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.,Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China.,Fudan University Taizhou Institute of Health Sciences, Taizhou, China
| | - Tingting Shi
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.,Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China.,Fudan University Taizhou Institute of Health Sciences, Taizhou, China
| | - Sheng Wu
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.,Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China.,Fudan University Taizhou Institute of Health Sciences, Taizhou, China
| | - Yiyun Xu
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.,Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China.,Fudan University Taizhou Institute of Health Sciences, Taizhou, China
| | - Chen Suo
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.,Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China.,Fudan University Taizhou Institute of Health Sciences, Taizhou, China
| | - Xingdong Chen
- Fudan University Taizhou Institute of Health Sciences, Taizhou, China.,State Key Laboratory of Genetic Engineering and Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China.,Department of Chronic Diseases Prevention, Taizhou Center for Disease Control and Prevention, Jiangsu, China.,Human Phenome Institute, Fudan University, Shanghai, China
| | - Tiejun Zhang
- Department of Epidemiology, School of Public Health, Fudan University, Shanghai, China.,Key Laboratory of Public Health Safety, Ministry of Education, Fudan University, Shanghai, China.,Fudan University Taizhou Institute of Health Sciences, Taizhou, China
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21
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Niu Y, Sui X, He Y, Xi H, Zhu R, Xu H, Li Y, Zhang Z, Guo L. Association between self-reported snoring and hypertension: a systematic review and meta-analysis. Sleep Med 2021; 88:140-148. [PMID: 34749273 DOI: 10.1016/j.sleep.2021.10.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 09/01/2021] [Accepted: 10/12/2021] [Indexed: 12/09/2022]
Abstract
OBJECTIVES The purpose of this study is to summarize the evidence for the association between snoring and hypertension and the effect of snoring on hypertension in men and/or women. METHODS We searched the articles in the Cochrane Library, PubMed, Scopus, Web of Science and Embase published up to 12 November 2020 to evaluate the association between snoring and hypertension. Studies were selected according to the predefined screening criteria and their qualities were assessed by Newcastle-Ottawa Quality Evaluation Scale. The odds ratio and 95% confidence interval were used as effective indicators. It was registered in PROSPERO with the number: CRD42021224912. RESULTS According to the inclusion/exclusion criteria, 11 studies including eight prospective cohort studies and three cross-sectional studies were included. The results showed that compared with non-snoring participants, snoring significantly increased the risk of hypertension in both men and women [odds ratio (OR) = 1.32, 95% confidence interval (CI), 1.23-1.42; men: odds ratio (OR) = 1.32; 95% confidence interval (CI), 1.18-1.49; women: odds ratio (OR) = 1.26; 95% confidence interval (CI), 1.14-1.40]. Besides, the risk of hypertension was significantly increased when the snoring frequency was ≥4 nights/week [frequency≥4 nights/week: odds ratio (OR) = 1.42; 95% confidence interval (CI), 1.21-1.66; 4 nights/week >frequency>0: odds ratio (OR) = 1.23; 95% confidence interval (CI),1.13-1.34]. CONCLUSIONS Snoring is considered as an independent predictor of hypertension in both men and women, which may play a role in the prevention and control of hypertension. People who snore frequently should pay close attention to their blood pressure levels to prevent hypertension.
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Affiliation(s)
- Yirou Niu
- School of Nursing, Jilin University, Changchun, Jilin, 130000, China.
| | - Xin Sui
- School of Nursing, Jilin University, Changchun, Jilin, 130000, China.
| | - Yayu He
- School of Nursing, Jilin University, Changchun, Jilin, 130000, China.
| | - Huihui Xi
- School of Nursing, Jilin University, Changchun, Jilin, 130000, China.
| | - Ruiting Zhu
- School of Nursing, Jilin University, Changchun, Jilin, 130000, China.
| | - Haiyan Xu
- School of Nursing, Jilin University, Changchun, Jilin, 130000, China.
| | - Yuewei Li
- School of Nursing, Jilin University, Changchun, Jilin, 130000, China.
| | - Zhuo Zhang
- School of Nursing, Jilin University, Changchun, Jilin, 130000, China.
| | - Lirong Guo
- School of Nursing, Jilin University, Changchun, Jilin, 130000, China.
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22
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Abstract
Sleep studies have typically followed criteria established many decades ago, but emerging technologies allow signal analyses that go far beyond the scoring rules for manual analysis of sleep recordings. These technologies may apply to the analysis of signals obtained in standard polysomnography in addition to novel signals more recently developed that provide both direct and indirect measures of sleep and breathing in the ambulatory setting. Automated analysis of signals such as electroencephalogram and oxygen saturation, in addition to heart rate and rhythm, provides a wealth of additional information on sleep and breathing disturbances and their potential for comorbidity.
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Affiliation(s)
- Walter T McNicholas
- Department of Respiratory and Sleep Medicine, School of Medicine, University College Dublin, St. Vincent's Hospital Group, Elm Park, Dublin 4, Ireland.
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23
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Devos P, Bruyneel M. IoT snoring sound detector prototype as a model of future participatory healthcare. Technol Health Care 2021; 30:491-496. [PMID: 34657858 DOI: 10.3233/thc-213145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Traditional healthcare is centred around providing in-hospital services using hospital owned medical instruments. The COVID-19 pandemic has shown that this approach lacks flexibility to insure follow-up and treatment of common medical problems. In an alternative setting adapted to this problem, participatory healthcare can be considered centred around data provided by patients owning and operating medical data collection equipment in their homes. OBJECTIVE In order to trigger such a shift reliable and price attractive devices need to become available. Snoring, as a human sound production during sleep, can reflect sleeping behaviour and indicate sleep problems as an element of the overall health condition of a person. METHODS The use of off-the-shelf hardware from Internet of Things platforms and standard audio components allows the development of such devices. A prototype of a snoring sound detector with this purpose is developed. RESULTS The device, controlled by the patient and with specific snoring recording and analysing functions is demonstrated as a model for future participatory healthcare. CONCLUSIONS Design of monitoring devices following this model could allow market introduction of new equipment for participatory healthcare, bringing a care complementary to traditional healthcare to the reach of patients, and could result in benefits from enhanced patient participation.
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Affiliation(s)
- Paul Devos
- WAVES Research Group, Department of Information Technology, Ghent University, Ghent, Belgium
| | - Marie Bruyneel
- Dept of Pneumology, CHU Saint Pierre, Université Libre de Bruxelles, Brussels, Belgium
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24
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Morinigo R, Quraishi SA, Ewing S, Azocar RJ, Schumann R. The B-APNEIC score: distilling the STOP-Bang questionnaire to identify patients at high risk for severe obstructive sleep apnoea. Anaesthesia 2021; 77:286-292. [PMID: 34473837 DOI: 10.1111/anae.15571] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/03/2021] [Indexed: 12/16/2022]
Abstract
The STOP-Bang questionnaire is an established clinical screening tool to identify the risk of having mild, moderate or severe obstructive sleep apnoea using eight variables. It is unclear whether all eight variables contribute equally to the risk of clinically significant obstructive sleep apnoea. We analysed each variable for its contribution to detecting obstructive sleep apnoea; based on the results, we investigated whether the STOP-Bang questionnaire could be abbreviated to identify patients at high risk for severe obstructive sleep apnoea. We recruited patients with suspected obstructive sleep apnoea who were referred for overnight polysomnography. We used multivariable logistic regression to investigate the association of STOP-Bang parameters with severe obstructive sleep apnoea based on clinical and polysomnography data. Regression estimates were used to select variables to create the novel B-APNEIC score. We constructed receiver operating characteristic curves for the STOP-Bang questionnaire and B-APNEIC scores to identify patients with severe obstructive sleep apnoea and compared the areas under the curve using the DeLong method. Of the 275 patients enrolled, 32% (n = 88) had severe obstructive sleep apnoea. Logistic regression demonstrated that neck circumference (OR 2.20; 95%CI 1.10-4.40, p = 0.03) was the only variable independently associated with severe obstructive sleep apnoea. Observed apnoea during sleep, blood pressure and body mass index were the three next most closely trending predictors of severe obstructive sleep apnoea and were included along with neck circumference in the B-APNEIC score. Receiver operating curves demonstrated that the areas under the curve for STOP-Bang vs. B-APNEIC were comparable for identifying patients with severe obstructive sleep apnoea (OR 0.75; 95%CI 0.68-0.81 vs. OR 0.75; 95%CI 0.68-0.81: p = 0.99, respectively). Our results suggest that the B-APNEIC score is a simplified adaptation of the STOP-Bang questionnaire with equivalent effectiveness in identifying patients with severe obstructive sleep apnoea. Further studies are needed to validate and build on our findings.
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Affiliation(s)
- R Morinigo
- Department of Anesthesiology and Peri-operative Medicine, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - S A Quraishi
- Department of Anesthesiology and Peri-operative Medicine, Tufts Medical Center, Tufts University School of Medicine, Boston, MA, USA
| | - S Ewing
- Department of Anesthesiology and Peri-operative Medicine, Tufts Medical Center, Boston, MA, USA
| | - R J Azocar
- Department of Anesthesiology and Peri-operative Medicine, Tufts Medical Center, Boston, MA, USA
| | - R Schumann
- Veterans Affairs Health Care System, Boston, MA, USA
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25
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Watson NF, Fernandez CR. Artificial intelligence and sleep: Advancing sleep medicine. Sleep Med Rev 2021; 59:101512. [PMID: 34166990 DOI: 10.1016/j.smrv.2021.101512] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 05/20/2021] [Accepted: 05/21/2021] [Indexed: 02/07/2023]
Abstract
Artificial intelligence (AI) allows analysis of "big data" combining clinical, environmental and laboratory based objective measures to allow a deeper understanding of sleep and sleep disorders. This development has the potential to transform sleep medicine in coming years to the betterment of patient care and our collective understanding of human sleep. This review addresses the current state of the field starting with a broad definition of the various components and analytic methods deployed in AI. We review examples of AI use in screening, endotyping, diagnosing, and treating sleep disorders and place this in the context of precision/personalized sleep medicine. We explore the opportunities for AI to both facilitate and extend providers' clinical impact and present ethical considerations regarding AI derived prognostic information. We cover early adopting specialties of AI in the clinical realm, such as radiology and pathology, to provide a road map for the challenges sleep medicine is likely to face when deploying this technology. Finally, we discuss pitfalls to ensure clinical AI implementation proceeds in the safest and most effective manner possible.
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Affiliation(s)
- Nathaniel F Watson
- Department of Neurology, University of Washington (UW) School of Medicine, USA; UW Medicine Sleep Center, USA.
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26
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Jiang Z, Qin J, Liang K, Zhao R, Yan F, Hou X, Wang C, Chen L. Self-reported snoring is associated with chronic kidney disease in obese but not in normal-weight Chinese adults. Ren Fail 2021; 43:709-717. [PMID: 33896382 PMCID: PMC8079005 DOI: 10.1080/0886022x.2021.1915332] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022] Open
Abstract
Background The relationship between sleeping disorders and chronic kidney disease (CKD) has already been reported. Snoring, a common clinical manifestation of obstructive sleep apnea–hypopnea syndrome, is of clinical value in assessing sleeping disorder severity. However, investigations of the connection between snoring and CKD are limited, especially in normal-weight populations. This study assessed the relationship between snoring frequency and CKD in obese and normal-weight people in China. Methods A community-based retrospective cross-sectional study of 3250 participants was performed. Study participants were divided into three groups – the regularly snoring group, occasionally snoring group, and never snoring group – based on their self-reported snoring frequency. CKD was defined as an estimated glomerular filtration rate of less than 60 mL/min/1.73 m2. Multiple logistic regression analysis was used to explore the relevance between snoring frequency and CKD prevalence. Results The CKD prevalence in obese participants was higher than that in normal-weight participants. Frequent snorers had a higher prevalence of CKD than those who were not frequent snorers in the obese group. Snoring frequency was correlated with CKD prevalence in obese participants independent of age, sex, smoking and drinking status, systolic blood pressure, triglyceride level, high-density lipoprotein, and homeostasis model assessment of insulin resistance (odds ratio: 2.66; 95% CI: 1.36–5.19; p=.004), while the same relationships did not exist in normal-weight participants (odds ratio: 0.79; 95% CI: 0.32–1.98; p=.614). Conclusions Snoring appears to be independently associated with CKD in obese but not in normal-weight Chinese adults.
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Affiliation(s)
- Ziyun Jiang
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, China.,Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, China.,Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, China
| | - Jun Qin
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, China.,Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, China.,Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, China
| | - Kai Liang
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, China.,Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, China.,Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, China
| | - Ruxing Zhao
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, China.,Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, China.,Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, China
| | - Fei Yan
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, China.,Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, China.,Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, China
| | - Xinguo Hou
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, China.,Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, China.,Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, China
| | - Chuan Wang
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, China.,Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, China.,Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, China
| | - Li Chen
- Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.,Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, China.,Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, China.,Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, China
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27
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Photoplethysmography in Normal and Pathological Sleep. SENSORS 2021; 21:s21092928. [PMID: 33922042 PMCID: PMC8122413 DOI: 10.3390/s21092928] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/19/2021] [Accepted: 04/20/2021] [Indexed: 01/20/2023]
Abstract
This article presents an overview of the advancements that have been made in the use of photoplethysmography (PPG) for unobtrusive sleep studies. PPG is included in the quickly evolving and very popular landscape of wearables but has specific interesting properties, particularly the ability to capture the modulation of the autonomic nervous system during sleep. Recent advances have been made in PPG signal acquisition and processing, including coupling it with accelerometry in order to construct hypnograms in normal and pathologic sleep and also to detect sleep-disordered breathing (SDB). The limitations of PPG (e.g., oxymetry signal failure, motion artefacts, signal processing) are reviewed as well as technical solutions to overcome these issues. The potential medical applications of PPG are numerous, including home-based detection of SDB (for triage purposes), and long-term monitoring of insomnia, circadian rhythm sleep disorders (to assess treatment effects), and treated SDB (to ensure disease control). New contact sensor combinations to improve future wearables seem promising, particularly tools that allow for the assessment of brain activity. In this way, in-ear EEG combined with PPG and actigraphy could be an interesting focus for future research.
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28
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O'Mahony AM, Garvey JF, McNicholas WT. Technologic advances in the assessment and management of obstructive sleep apnoea beyond the apnoea-hypopnoea index: a narrative review. J Thorac Dis 2020; 12:5020-5038. [PMID: 33145074 PMCID: PMC7578472 DOI: 10.21037/jtd-sleep-2020-003] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Obstructive sleep apnoea (OSA) is a growing and serious worldwide health problem with significant health and socioeconomic consequences. Current diagnostic testing strategies are limited by cost, access to resources and over reliance on one measure, namely the apnoea-hypopnoea frequency per hour (AHI). Recent evidence supports moving away from the AHI as the principle measure of OSA severity towards a more personalised approach to OSA diagnosis and treatment that includes phenotypic and biological traits. Novel advances in technology include the use of signals such as heart rate variability (HRV), oximetry and peripheral arterial tonometry (PAT) as alternative or additional measures. Ubiquitous use of smartphones and developments in wearable technology have also led to increased availability of applications and devices to facilitate home screening of at-risk populations, although current evidence indicates relatively poor accuracy in comparison with the traditional gold standard polysomnography (PSG). In this review, we evaluate the current strategies for diagnosing OSA in the context of their limitations, potential physiological targets as alternatives to AHI and the role of novel technology in OSA. We also evaluate the current evidence for using newer technologies in OSA diagnosis, the physiological targets such as smartphone applications and wearable technology. Future developments in OSA diagnosis and assessment will likely focus increasingly on systemic effects of sleep disordered breathing (SDB) such as changes in nocturnal oxygen and blood pressure (BP); and may also include other factors such as circulating biomarkers. These developments will likely require a re-evaluation of the diagnostic and grading criteria for clinically significant OSA.
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Affiliation(s)
- Anne M O'Mahony
- School of Medicine, University College Dublin, Dublin, Ireland
| | - John F Garvey
- School of Medicine, University College Dublin, Dublin, Ireland
| | - Walter T McNicholas
- School of Medicine, University College Dublin, Dublin, Ireland.,First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
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