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Talukder A, Li Y, Yeung D, Shi M, Umbach DM, Fan Z, Li L. OSApredictor: A tool for prediction of moderate to severe obstructive sleep apnea-hypopnea using readily available patient characteristics. Comput Biol Med 2024; 178:108777. [PMID: 38901189 PMCID: PMC11265974 DOI: 10.1016/j.compbiomed.2024.108777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 05/25/2024] [Accepted: 06/15/2024] [Indexed: 06/22/2024]
Abstract
Sleep apnea is a common sleep disorder. The availability of an easy-to-use sleep apnea predictor would provide a public health benefit by promoting early diagnosis and treatment. Our goal was to develop a prediction tool that used commonly available variables and was accessible to the public through a web site. Using data from polysomnography (PSG) studies that measured the apnea-hypopnea index (AHI), we built a machine learning tool to predict the presence of moderate to severe obstructive sleep apnea (OSA) (defined as AHI ≥15). Our tool employs only seven widely available predictor variables: age, sex, weight, height, pulse oxygen saturation, heart rate and respiratory rate. As a preliminary step, we used 16,958 PSG studies to examine eight machine learning algorithms via five-fold cross validation and determined that XGBoost exhibited superior predictive performance. We then refined the XGBoost predictor by randomly partitioning the data into a training and a test set (13,566 and 3392 PSGs, respectively) and repeatedly subsampling from the training set to construct 1000 training subsets. We evaluated each of the resulting 1000 XGBoost models on the single set-aside test set. The resulting classification tool correctly identified 72.5 % of those with moderate to severe OSA as having the condition (sensitivity) and 62.8 % of those without moderate to-severe OSA as not having it (specificity); overall accuracy was 66 %. We developed a user-friendly publicly available website (https://manticore.niehs.nih.gov/OSApredictor). We hope that our easy-to-use tool will serve as a screening vehicle that enables more patients to be clinically diagnosed and treated for OSA.
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Affiliation(s)
- Amlan Talukder
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Yuanyuan Li
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Deryck Yeung
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Min Shi
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - David M Umbach
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, USA
| | - Zheng Fan
- Division of Sleep Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Neurology, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Leping Li
- Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, 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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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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Cho SW, Jung SJ, Shin JH, Won TB, Rhee CS, Kim JW. Evaluating Prediction Models of Sleep Apnea From Smartphone-Recorded Sleep Breathing Sounds. JAMA Otolaryngol Head Neck Surg 2022; 148:515-521. [PMID: 35420648 PMCID: PMC9011176 DOI: 10.1001/jamaoto.2022.0244] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Importance Breathing sounds during sleep are an important characteristic feature of obstructive sleep apnea (OSA) and have been regarded as a potential biomarker. Breathing sounds during sleep can be easily recorded using a microphone, which is found in most smartphone devices. Therefore, it may be easy to implement an evaluation tool for prescreening purposes. Objective To evaluate OSA prediction models using smartphone-recorded sounds and identify optimal settings with regard to noise processing and sound feature selection. Design, Setting, and Participants A cross-sectional study was performed among patients who visited the sleep center of Seoul National University Bundang Hospital for snoring or sleep apnea from August 2015 to August 2019. Audio recordings during sleep were performed using a smartphone during routine, full-night, in-laboratory polysomnography. Using a random forest algorithm, binary classifications were separately conducted for 3 different threshold criteria according to an apnea hypopnea index (AHI) threshold of 5, 15, or 30 events/h. Four regression models were created according to noise reduction and feature selection from the input sound to predict actual AHI: (1) noise reduction without feature selection, (2) noise reduction with feature selection, (3) neither noise reduction nor feature selection, and (4) feature selection without noise reduction. Clinical and polysomnographic parameters that may have been associated with errors were assessed. Data were analyzed from September 2019 to September 2020. Main Outcomes and Measures Accuracy of OSA prediction models. Results A total of 423 patients (mean [SD] age, 48.1 [12.8] years; 356 [84.1%] male) were analyzed. Data were split into training (n = 256 [60.5%]) and test data sets (n = 167 [39.5%]). Accuracies were 88.2%, 82.3%, and 81.7%, and the areas under curve were 0.90, 0.89, and 0.90 for an AHI threshold of 5, 15, and 30 events/h, respectively. In the regression analysis, using recorded sounds that had not been denoised and had only selected attributes resulted in the highest correlation coefficient (r = 0.78; 95% CI, 0.69-0.88). The AHI (β = 0.33; 95% CI, 0.24-0.42) and sleep efficiency (β = -0.20; 95% CI, -0.35 to -0.05) were found to be associated with estimation error. Conclusions and Relevance In this cross-sectional study, recorded sleep breathing sounds using a smartphone were used to create reasonably accurate OSA prediction models. Future research should focus on real-life recordings using various smartphone devices.
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Affiliation(s)
- Sung-Woo Cho
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea
| | - Sung Jae Jung
- Big Data Center, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Jin Ho Shin
- Big Data Center, Seoul National University Bundang Hospital, Seongnam, Korea
| | - Tae-Bin Won
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.,Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
| | - Chae-Seo Rhee
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.,Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea.,Sensory Organ Research Institute, Seoul National University Medical Research Center, Seoul, Korea
| | - Jeong-Whun Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.,Sensory Organ Research Institute, Seoul National University Medical Research Center, Seoul, Korea
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Kim JW, Shin J, Lee K, Won TB, Rhee CS, Cho SW. Prediction of Oxygen Desaturation by Using Sound Data From a Noncontact Device: A Proof-of-Concept Study. Laryngoscope 2021; 132:901-905. [PMID: 34873695 DOI: 10.1002/lary.29971] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 11/04/2021] [Accepted: 11/24/2021] [Indexed: 11/11/2022]
Abstract
OBJECTIVES/HYPOTHESIS Prediction of the apnea-hypopnea index (AHI) from breathing sounds during sleep could be used to prescreen for obstructive sleep apnea (OSA). In addition, the oxygen desaturation index (ODI) is a known risk factor for developing cardiovascular disease in OSA patients. This study focused on estimation of ODI from a noncontact manner from sleep breathing sounds. STUDY DESIGN Retrospective study. METHODS Patients who visited the sleep center due to snoring or sleep apnea underwent polysomnography in lab overnight. Sound recordings were made during polysomnography using a microphone. After noise reduction, the sound data were segmented into 5 seconds windows and features were extracted. Binary classification and regression analyses were performed to estimate the ODI during sleep (model 1). This was re-tested after inclusion of body mass index (BMI) and age as additional features (model 2: BMI only, model 3: BMI and age). RESULTS We included 116 patients. The mean age and AHI of all patients were 50.4 ± 16.7 years and 23.0 ± 24.0 events/hr. In binary classification, for ODI cutoff values of 5, 15, and 30 events/hr, the areas under the curve were 0.88, 0.93, 0.91, respectively, and accuracies were 85.34, 86.21, and 87.07, respectively. In regression analysis, the correlation coefficient and mean absolute error were 0.80 and 9.60 events/hr, respectively. In models 2 and 3, the correlation coefficient and mean absolute error were 0.82, 9.44 events/hr and 0.81, 9.6 events/hr, respectively. CONCLUSION Prediction of ODI from sleep sound seems to be feasible. Additional clinical feature such as BMI may increase overall predictability. LEVEL OF EVIDENCE IV Laryngoscope, 2021.
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Affiliation(s)
- Jeong-Whun Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea.,Sensory Organ Research Institute, Seoul National University Medical Research Center, Seoul National University Medical Research Center, Seoul, Korea
| | - Jaeyoung Shin
- Music and Audio Research Group, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, South Korea
| | - Kyogu Lee
- Music and Audio Research Group, Graduate School of Convergence Science and Technology, Seoul National University, Suwon, South Korea
| | - Tae-Bin Won
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea.,Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Chae-Seo Rhee
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea.,Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea.,Sensory Organ Research Institute, Seoul National University Medical Research Center, Seoul National University Medical Research Center, Seoul, Korea
| | - Sung-Woo Cho
- Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea
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Serrano Alarcón Á, Martínez Madrid N, Seepold R. A Minimum Set of Physiological Parameters to Diagnose Obstructive Sleep Apnea Syndrome Using Non-Invasive Portable Monitors. A Systematic Review. Life (Basel) 2021; 11:1249. [PMID: 34833126 PMCID: PMC8623368 DOI: 10.3390/life11111249] [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] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/08/2021] [Accepted: 11/13/2021] [Indexed: 11/17/2022] Open
Abstract
Introduction. Despite its high accuracy, polysomnography (PSG) has several drawbacks for diagnosing obstructive sleep apnea (OSA). Consequently, multiple portable monitors (PMs) have been proposed. Objective. This systematic review aims to investigate the current literature to analyze the sets of physiological parameters captured by a PM to select the minimum number of such physiological signals while maintaining accurate results in OSA detection. Methods. Inclusion and exclusion criteria for the selection of publications were established prior to the search. The evaluation of the publications was made based on one central question and several specific questions. Results. The abilities to detect hypopneas, sleep time, or awakenings were some of the features studied to investigate the full functionality of the PMs to select the most relevant set of physiological signals. Based on the physiological parameters collected (one to six), the PMs were classified into sets according to the level of evidence. The advantages and the disadvantages of each possible set of signals were explained by answering the research questions proposed in the methods. Conclusions. The minimum number of physiological signals detected by PMs for the detection of OSA depends mainly on the purpose and context of the sleep study. The set of three physiological signals showed the best results in the detection of OSA.
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Affiliation(s)
- Ángel Serrano Alarcón
- School of Informatics, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany;
| | - Natividad Martínez Madrid
- School of Informatics, Reutlingen University, Alteburgstr. 150, 72762 Reutlingen, Germany;
- Institute of Digital Medicine, I.M. Sechenov First Moscow State Medical University, 2-4 Bolshaya Pirogovskaya st., 119435 Moscow, Russian Federation;
| | - Ralf Seepold
- Institute of Digital Medicine, I.M. Sechenov First Moscow State Medical University, 2-4 Bolshaya Pirogovskaya st., 119435 Moscow, Russian Federation;
- HTWG Konstanz, Department of Computer Science, Alfred-Wachtel-Str. 8, 78462 Konstanz, Germany
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Tama BA, Kim DH, Kim G, Kim SW, Lee S. Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery. Clin Exp Otorhinolaryngol 2020; 13:326-339. [PMID: 32631041 PMCID: PMC7669308 DOI: 10.21053/ceo.2020.00654] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 05/24/2020] [Accepted: 06/09/2020] [Indexed: 12/12/2022] Open
Abstract
This study presents an up-to-date survey of the use of artificial intelligence (AI) in the field of otorhinolaryngology, considering opportunities, research challenges, and research directions. We searched PubMed, the Cochrane Central Register of Controlled Trials, Embase, and the Web of Science. We initially retrieved 458 articles. The exclusion of non-English publications and duplicates yielded a total of 90 remaining studies. These 90 studies were divided into those analyzing medical images, voice, medical devices, and clinical diagnoses and treatments. Most studies (42.2%, 38/90) used AI for image-based analysis, followed by clinical diagnoses and treatments (24 studies). Each of the remaining two subcategories included 14 studies. Machine learning and deep learning have been extensively applied in the field of otorhinolaryngology. However, the performance of AI models varies and research challenges remain.
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Affiliation(s)
- Bayu Adhi Tama
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Do Hyun Kim
- Department of Otolaryngology-Head and Neck Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Gyuwon Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
| | - Soo Whan Kim
- Department of Otolaryngology-Head and Neck Surgery, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Seungchul Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
- Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, Korea
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