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Chang JL, Goldberg AN, Alt JA, Alzoubaidi M, Ashbrook L, Auckley D, Ayappa I, Bakhtiar H, Barrera JE, Bartley BL, Billings ME, Boon MS, Bosschieter P, Braverman I, Brodie K, Cabrera-Muffly C, Caesar R, Cahali MB, Cai Y, Cao M, Capasso R, Caples SM, Chahine LM, Chang CP, Chang KW, Chaudhary N, Cheong CSJ, Chowdhuri S, Cistulli PA, Claman D, Collen J, Coughlin KC, Creamer J, Davis EM, Dupuy-McCauley KL, Durr ML, Dutt M, Ali ME, Elkassabany NM, Epstein LJ, Fiala JA, Freedman N, Gill K, Boyd Gillespie M, Golisch L, Gooneratne N, Gottlieb DJ, Green KK, Gulati A, Gurubhagavatula I, Hayward N, Hoff PT, Hoffmann OM, Holfinger SJ, Hsia J, Huntley C, Huoh KC, Huyett P, Inala S, Ishman SL, Jella TK, Jobanputra AM, Johnson AP, Junna MR, Kado JT, Kaffenberger TM, Kapur VK, Kezirian EJ, Khan M, Kirsch DB, Kominsky A, Kryger M, Krystal AD, Kushida CA, Kuzniar TJ, Lam DJ, Lettieri CJ, Lim DC, Lin HC, Liu SY, MacKay SG, Magalang UJ, Malhotra A, Mansukhani MP, Maurer JT, May AM, Mitchell RB, Mokhlesi B, Mullins AE, Nada EM, Naik S, Nokes B, Olson MD, Pack AI, Pang EB, Pang KP, Patil SP, Van de Perck E, Piccirillo JF, Pien GW, Piper AJ, Plawecki A, Quigg M, Ravesloot MJ, Redline S, Rotenberg BW, Ryden A, Sarmiento KF, Sbeih F, Schell AE, Schmickl CN, Schotland HM, Schwab RJ, Seo J, Shah N, Shelgikar AV, Shochat I, Soose RJ, Steele TO, Stephens E, Stepnowsky C, Strohl KP, Sutherland K, Suurna MV, Thaler E, Thapa S, Vanderveken OM, de Vries N, Weaver EM, Weir ID, Wolfe LF, Tucker Woodson B, Won CH, Xu J, Yalamanchi P, Yaremchuk K, Yeghiazarians Y, Yu JL, Zeidler M, Rosen IM. International Consensus Statement on Obstructive Sleep Apnea. Int Forum Allergy Rhinol 2023; 13:1061-1482. [PMID: 36068685 PMCID: PMC10359192 DOI: 10.1002/alr.23079] [Citation(s) in RCA: 53] [Impact Index Per Article: 53.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Revised: 08/12/2022] [Accepted: 08/18/2022] [Indexed: 11/08/2022]
Abstract
BACKGROUND Evaluation and interpretation of the literature on obstructive sleep apnea (OSA) allows for consolidation and determination of the key factors important for clinical management of the adult OSA patient. Toward this goal, an international collaborative of multidisciplinary experts in sleep apnea evaluation and treatment have produced the International Consensus statement on Obstructive Sleep Apnea (ICS:OSA). METHODS Using previously defined methodology, focal topics in OSA were assigned as literature review (LR), evidence-based review (EBR), or evidence-based review with recommendations (EBR-R) formats. Each topic incorporated the available and relevant evidence which was summarized and graded on study quality. Each topic and section underwent iterative review and the ICS:OSA was created and reviewed by all authors for consensus. RESULTS The ICS:OSA addresses OSA syndrome definitions, pathophysiology, epidemiology, risk factors for disease, screening methods, diagnostic testing types, multiple treatment modalities, and effects of OSA treatment on multiple OSA-associated comorbidities. Specific focus on outcomes with positive airway pressure (PAP) and surgical treatments were evaluated. CONCLUSION This review of the literature consolidates the available knowledge and identifies the limitations of the current evidence on OSA. This effort aims to create a resource for OSA evidence-based practice and identify future research needs. Knowledge gaps and research opportunities include improving the metrics of OSA disease, determining the optimal OSA screening paradigms, developing strategies for PAP adherence and longitudinal care, enhancing selection of PAP alternatives and surgery, understanding health risk outcomes, and translating evidence into individualized approaches to therapy.
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Affiliation(s)
- Jolie L. Chang
- University of California, San Francisco, California, USA
| | | | | | | | - Liza Ashbrook
- University of California, San Francisco, California, USA
| | | | - Indu Ayappa
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | | | | | - Maurits S. Boon
- Sidney Kimmel Medical Center at Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Pien Bosschieter
- Academic Centre for Dentistry Amsterdam, Amsterdam, The Netherlands
| | - Itzhak Braverman
- Hillel Yaffe Medical Center, Hadera Technion, Faculty of Medicine, Hadera, Israel
| | - Kara Brodie
- University of California, San Francisco, California, USA
| | | | - Ray Caesar
- Stone Oak Orthodontics, San Antonio, Texas, USA
| | | | - Yi Cai
- University of California, San Francisco, California, USA
| | | | | | | | | | | | | | | | | | - Susmita Chowdhuri
- Wayne State University and John D. Dingell VA Medical Center, Detroit, Michigan, USA
| | - Peter A. Cistulli
- Faculty of Medicine and Health, University of Sydney, Sydney, Australia
| | - David Claman
- University of California, San Francisco, California, USA
| | - Jacob Collen
- Uniformed Services University, Bethesda, Maryland, USA
| | | | | | - Eric M. Davis
- University of Virginia, Charlottesville, Virginia, USA
| | | | | | - Mohan Dutt
- University of Michigan, Ann Arbor, Michigan, USA
| | - Mazen El Ali
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | | | | | | | - Kirat Gill
- Stanford University, Palo Alto, California, USA
| | | | - Lea Golisch
- University Hospital Mannheim, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | | | | | | | - Arushi Gulati
- University of California, San Francisco, California, USA
| | | | | | - Paul T. Hoff
- University of Michigan, Ann Arbor, Michigan, USA
| | - Oliver M.G. Hoffmann
- University Hospital Mannheim, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | | | - Jennifer Hsia
- University of Minnesota, Minneapolis, Minnesota, USA
| | - Colin Huntley
- Sidney Kimmel Medical Center at Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | | | | | - Sanjana Inala
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | | | | | | | | | | | | | | | - Meena Khan
- Ohio State University, Columbus, Ohio, USA
| | | | - Alan Kominsky
- Cleveland Clinic Head and Neck Institute, Cleveland, Ohio, USA
| | - Meir Kryger
- Yale School of Medicine, New Haven, Connecticut, USA
| | | | | | | | - Derek J. Lam
- Oregon Health and Science University, Portland, Oregon, USA
| | | | | | | | | | | | | | - Atul Malhotra
- University of California, San Diego, California, USA
| | | | - Joachim T. Maurer
- University Hospital Mannheim, Ruprecht-Karls-University Heidelberg, Heidelberg, Germany
| | - Anna M. May
- Case Western Reserve University, Cleveland, Ohio, USA
| | - Ron B. Mitchell
- University of Texas, Southwestern and Children’s Medical Center Dallas, Texas, USA
| | | | | | | | | | - Brandon Nokes
- University of California, San Diego, California, USA
| | | | - Allan I. Pack
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | | | | | | | | | | | | | | | | | - Mark Quigg
- University of Virginia, Charlottesville, Virginia, USA
| | | | - Susan Redline
- Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Armand Ryden
- Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California, USA
| | | | - Firas Sbeih
- Cleveland Clinic Head and Neck Institute, Cleveland, Ohio, USA
| | | | | | | | | | - Jiyeon Seo
- University of California, Los Angeles, California, USA
| | - Neomi Shah
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | - Ryan J. Soose
- University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | | | - Erika Stephens
- University of California, San Francisco, California, USA
| | | | | | | | | | - Erica Thaler
- University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Sritika Thapa
- Yale School of Medicine, New Haven, Connecticut, USA
| | | | - Nico de Vries
- Academic Centre for Dentistry Amsterdam, Amsterdam, The Netherlands
| | | | - Ian D. Weir
- Yale School of Medicine, New Haven, Connecticut, USA
| | | | | | | | - Josie Xu
- University of Toronto, Ontario, Canada
| | | | | | | | | | | | - Ilene M. Rosen
- University of Pennsylvania, Philadelphia, Pennsylvania, 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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Martín-González S, Ravelo-García AG, Navarro-Mesa JL, Hernández-Pérez E. Combining Heart Rate Variability and Oximetry to Improve Apneic Event Screening in Non-Desaturating Patients. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094267. [PMID: 37177472 PMCID: PMC10181515 DOI: 10.3390/s23094267] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023]
Abstract
In this paper, we thoroughly analyze the detection of sleep apnea events in the context of Obstructive Sleep Apnea (OSA), which is considered a public health problem because of its high prevalence and serious health implications. We especially evaluate patients who do not always show desaturations during apneic episodes (non-desaturating patients). For this purpose, we use a database (HuGCDN2014-OXI) that includes desaturating and non-desaturating patients, and we use the widely used Physionet Apnea Dataset for a meaningful comparison with prior work. Our system combines features extracted from the Heart-Rate Variability (HRV) and SpO2, and it explores their potential to characterize desaturating and non-desaturating events. The HRV-based features include spectral, cepstral, and nonlinear information (Detrended Fluctuation Analysis (DFA) and Recurrence Quantification Analysis (RQA)). SpO2-based features include temporal (variance) and spectral information. The features feed a Linear Discriminant Analysis (LDA) classifier. The goal is to evaluate the effect of using these features either individually or in combination, especially in non-desaturating patients. The main results for the detection of apneic events are: (a) Physionet success rate of 96.19%, sensitivity of 95.74% and specificity of 95.25% (Area Under Curve (AUC): 0.99); (b) HuGCDN2014-OXI of 87.32%, 83.81% and 88.55% (AUC: 0.934), respectively. The best results for the global diagnosis of OSA patients (HuGCDN2014-OXI) are: success rate of 95.74%, sensitivity of 100%, and specificity of 89.47%. We conclude that combining both features is the most accurate option, especially when there are non-desaturating patterns among the recordings under study.
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Affiliation(s)
- Sofía Martín-González
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Antonio G Ravelo-García
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
- Interactive Technologies Institute (ITI/LARSyS and ARDITI), 9020-105 Funchal, Portugal
| | - Juan L Navarro-Mesa
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
| | - Eduardo Hernández-Pérez
- Institute for Technological Development and Innovation in Communications, Universidad de Las Palmas de Gran Canaria, 35017 Las Palmas de Gran Canaria, Spain
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Balci M, Tasdemir S, Ozmen G, Golcuk A. Machine Learning-Based Detection of Sleep-Disordered Breathing Type Using Time and Time-Frequency Features. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103402] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Álvarez D, Gutiérrez-Tobal GC, Vaquerizo-Villar F, Moreno F, Del Campo F, Hornero R. Oximetry Indices in the Management of Sleep Apnea: From Overnight Minimum Saturation to the Novel Hypoxemia Measures. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1384:219-239. [PMID: 36217087 DOI: 10.1007/978-3-031-06413-5_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Obstructive sleep apnea (OSA) is a multidimensional disease often underdiagnosed due to the complexity and unavailability of its standard diagnostic method: the polysomnography. Among the alternative abbreviated tests searching for a compromise between simplicity and accurateness, oximetry is probably the most popular. The blood oxygen saturation (SpO2) signal is characterized by a near-constant profile in healthy subjects breathing normally, while marked drops (desaturations) are linked to respiratory events. Parameterization of the desaturations has led to a great number of indices of severity assessment commonly used to assist in OSA diagnosis. In this chapter, the main methodologies used to characterize the overnight oximetry profile are reviewed, from visual inspection and simple statistics to complex measures involving signal processing and pattern recognition techniques. We focus on the individual performance of each approach, but also on the complementarity among the great amount of indices existing in the state of the art, looking for the most relevant oximetric feature subset. Finally, a quick overview of SpO2-based deep learning applications for OSA management is carried out, where the raw oximetry signal is analyzed without previous parameterization. Our research allows us to conclude that all the methodologies (conventional, time, frequency, nonlinear, and hypoxemia-based) demonstrate high ability to provide relevant oximetric indices, but only a reduced set provide non-redundant complementary information leading to a significant performance increase. Finally, although oximetry is a robust tool, greater standardization and prospective validation of the measures derived from complex signal processing techniques are still needed to homogenize interpretation and increase generalizability.
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Affiliation(s)
- Daniel Álvarez
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain.
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain.
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain.
| | - Gonzalo C Gutiérrez-Tobal
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Fernando Vaquerizo-Villar
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Fernando Moreno
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
| | - Félix Del Campo
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Pneumology Department, Río Hortega University Hospital, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group (GIB), University of Valladolid, Valladolid, Spain
- Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Valladolid, Spain
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Classification of Obstructive Sleep Apnoea from single-lead ECG signals using convolutional neural and Long Short Term Memory networks. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102906] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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7
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Application of automatic detection based on overnight airflow and blood oxygen in patients with sleep disordered breathing. Eur Arch Otorhinolaryngol 2020; 278:873-881. [PMID: 32409858 DOI: 10.1007/s00405-020-06008-5] [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: 02/24/2020] [Accepted: 04/24/2020] [Indexed: 10/24/2022]
Abstract
PURPOSE To explore the feasibility of automatic detection based on air flow and blood oxygen in patients with sleep disordered breathing. METHODS This study proposes a new automated detection method for sleep disordered breathing based on overnight airflow and blood oxygen saturation (SaO2). In this regard, local range (LR) of the airflow was adopted to detect apnea events and the SaO2 sudden drops were used to help determine hypopnea events. Pearson correlation index was used to evaluate the relationship between the two automated methods (this study vs. Remlogic software) and the manual reports. Error and mean absolute error (MAE) were used to assess the two automated methods. RESULTS For all patients, the apnea-hypopnea index (AHI), apnea index (AI) and hypopnea index (HI) for our automated scoring and manual reports were highly correlated (the Pearson correlation index were 0.996, 0.995 and 0.928, respectively, P < 0.001). However, HI for Remlogic automated scoring and clinical manual reports was poorly correlated (r = 0.316, P < 0.001). Compared with the manual reports, mean absolute error of AHI, AI and HI between the two automated methods (this study vs. Remlogic software) were statistically significant (P < 0.0001). Furthermore, among the three subgroups (group 1, AHI < 15/h, group 2, 15/h ≤ AHI < 30/h and group 3, AHI ≥ 30/h), the mean error and MAE of AHI between the two automated methods were also statistically significant (P < 0.01). CONCLUSIONS Generally, good agreements were shown between our automated detection and clinical reports. This procedure is robust and effective, which would significantly shorten the analysis time.
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Rashid NHA, Zaghi S, Scapuccin M, Camacho M, Certal V, Capasso R. The Value of Oxygen Desaturation Index for Diagnosing Obstructive Sleep Apnea: A Systematic Review. Laryngoscope 2020; 131:440-447. [DOI: 10.1002/lary.28663] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 03/07/2020] [Accepted: 03/13/2020] [Indexed: 11/09/2022]
Affiliation(s)
- Nur HA Rashid
- Unit of Otorhinolaryngology, Department of Surgery, Faculty of Medicine and Health Sciences Universiti Putra Malaysia Serdang Malaysia
| | - Soroush Zaghi
- University of California Los Angeles (UCLA) Medical Center, Santa Monica Santa Monica California USA
| | - Marcelo Scapuccin
- Department of Otorhinolaryngology‐Head and Neck Surgery Santa Casa School of Medicine Sao Paulo Brazil
| | - Macario Camacho
- Division of Sleep Surgery and Medicine, Department of Otolaryngology‐Head and Neck Surgery Tripler Army Medical Center Honolulu Hawaii USA
| | - Victor Certal
- Department of Otorhinolaryngology Sleep Medicine Centre, Hospital CUF Porto Porto Portugal
| | - Robson Capasso
- Division of Sleep Surgery Department of Otolaryngology‐Head & Neck Surgery, Stanford University School of Medicine
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Cao W, Luo J, Xiao Y. A Review of Current Tools Used for Evaluating the Severity of Obstructive Sleep Apnea. Nat Sci Sleep 2020; 12:1023-1031. [PMID: 33239929 PMCID: PMC7680675 DOI: 10.2147/nss.s275252] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 10/08/2020] [Indexed: 12/21/2022] Open
Abstract
Obstructive sleep apnea (OSA) is a common and heterogeneous disease characterized by episodic collapse within the upper airways, which leads to reduced ventilation and adverse consequences, including hypoxia, hypercapnia, sleep fragmentation, and long-term effects such as cardiovascular comorbidities. The clinical diagnosis of OSA and its severity classification are often determined based on the apnea-hypopnea index (AHI), defining the number of apneic and hypopnea events per hour of sleep. However, the limitations of the AHI to assess disease severity have necessitated the exploration of other metrics for additional information to reflect the complexity of OSA. Novel parameters such as the hypoxic burden have the potential to better capture the main features of OSA by maximizing the information available from the polysomnogram. These emerging measures have described multidimensional qualities of sleep-disordered breathing events and breathing irregularity and will ultimately result in better management of OSA.
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Affiliation(s)
- Wenhao Cao
- Department of Respiratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Jinmei Luo
- Department of Respiratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
| | - Yi Xiao
- Department of Respiratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, People's Republic of China
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Álvarez D, Sánchez-Fernández A, Andrés-Blanco AM, Gutiérrez-Tobal GC, Vaquerizo-Villar F, Barroso-García V, Hornero R, del Campo F. Influence of Chronic Obstructive Pulmonary Disease and Moderate-To-Severe Sleep Apnoea in Overnight Cardiac Autonomic Modulation: Time, Frequency and Non-Linear Analyses. ENTROPY 2019; 21:e21040381. [PMID: 33267095 PMCID: PMC7514865 DOI: 10.3390/e21040381] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 04/02/2019] [Accepted: 04/05/2019] [Indexed: 11/25/2022]
Abstract
Chronic obstructive pulmonary disease (COPD) is one of the most prevalent lung diseases worldwide. COPD patients show major dysfunction in cardiac autonomic modulation due to sustained hypoxaemia, which has been significantly related to higher risk of cardiovascular disease. Obstructive sleep apnoea syndrome (OSAS) is a frequent comorbidity in COPD patients. It has been found that patients suffering from both COPD and OSAS simultaneously, the so-called overlap syndrome, have notably higher morbidity and mortality. Heart rate variability (HRV) has demonstrated to be useful to assess changes in autonomic functioning in different clinical conditions. However, there is still little scientific evidence on the magnitude of changes in cardiovascular dynamics elicited by the combined effect of both respiratory diseases, particularly during sleep, when apnoeic events occur. In this regard, we hypothesised that a non-linear analysis is able to provide further insight into long-term dynamics of overnight cardiovascular modulation. Accordingly, this study is aimed at assessing the usefulness of sample entropy (SampEn) to distinguish changes in overnight pulse rate variability (PRV) recordings among three patient groups while sleeping: COPD, moderate-to-severe OSAS, and overlap syndrome. In order to achieve this goal, a population composed of 297 patients were studied: 22 with COPD alone, 213 showing moderate-to-severe OSAS, and 62 with COPD and moderate-to-severe OSAS simultaneously (COPD+OSAS). Cardiovascular dynamics were analysed using pulse rate (PR) recordings from unattended pulse oximetry carried out at patients’ home. Conventional time- and frequency- domain analyses were performed to characterise sympathetic and parasympathetic activation of the nervous system, while SampEn was applied to quantify long-term changes in irregularity. Our analyses revealed that overnight PRV recordings from COPD+OSAS patients were significantly more irregular (higher SampEn) than those from patients with COPD alone (0.267 [0.210–0.407] vs. 0.212 [0.151–0.267]; p < 0.05) due to recurrent apnoeic events during the night. Similarly, COPD + OSAS patients also showed significantly higher irregularity in PRV during the night than subjects with OSAS alone (0.267 [0.210–0.407] vs. 0.241 [0.189–0.325]; p = 0.05), which suggests that the cumulative effect of both diseases increases disorganization of pulse rate while sleeping. On the other hand, no statistical significant differences were found between COPD and COPD + OSAS patients when traditional frequency bands (LF and HF) were analysed. We conclude that SampEn is able to properly quantify changes in overnight cardiovascular dynamics of patients with overlap syndrome, which could be useful to assess cardiovascular impairment in COPD patients due to the presence of concomitant OSAS.
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Affiliation(s)
- Daniel Álvarez
- Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, c/ Dulzaina 2, 47012 Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
- Correspondence: ; Tel.: +34-983-420400 (ext. 85776)
| | - Ana Sánchez-Fernández
- Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, c/ Dulzaina 2, 47012 Valladolid, Spain
| | - Ana M. Andrés-Blanco
- Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, c/ Dulzaina 2, 47012 Valladolid, Spain
| | | | | | - Verónica Barroso-García
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
| | - Félix del Campo
- Sleep-Ventilation Unit, Pneumology Service, Río Hortega University Hospital, c/ Dulzaina 2, 47012 Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Paseo de Belén 15, 47011 Valladolid, Spain
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11
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Uddin MB, Chow CM, Su SW. Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review. Physiol Meas 2018; 39:03TR01. [DOI: 10.1088/1361-6579/aaafb8] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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12
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Andrés-Blanco AM, Álvarez D, Crespo A, Arroyo CA, Cerezo-Hernández A, Gutiérrez-Tobal GC, Hornero R, del Campo F. Assessment of automated analysis of portable oximetry as a screening test for moderate-to-severe sleep apnea in patients with chronic obstructive pulmonary disease. PLoS One 2017; 12:e0188094. [PMID: 29176802 PMCID: PMC5703515 DOI: 10.1371/journal.pone.0188094] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2017] [Accepted: 10/31/2017] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The coexistence of obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD) leads to increased morbidity and mortality. The development of home-based screening tests is essential to expedite diagnosis. Nevertheless, there is still very limited evidence on the effectiveness of portable monitoring to diagnose OSAS in patients with pulmonary comorbidities. OBJECTIVE To assess the influence of suffering from COPD in the performance of an oximetry-based screening test for moderate-to-severe OSAS, both in the hospital and at home. METHODS A total of 407 patients showing moderate-to-high clinical suspicion of OSAS were involved in the study. All subjects underwent (i) supervised portable oximetry simultaneously to in-hospital polysomnography (PSG) and (ii) unsupervised portable oximetry at home. A regression-based multilayer perceptron (MLP) artificial neural network (ANN) was trained to estimate the apnea-hypopnea index (AHI) from portable oximetry recordings. Two independent validation datasets were analyzed: COPD versus non-COPD. RESULTS The portable oximetry-based MLP ANN reached similar intra-class correlation coefficient (ICC) values between the estimated AHI and the actual AHI for the non-COPD and the COPD groups either in the hospital (non-COPD: 0.937, 0.909-0.956 CI95%; COPD: 0.936, 0.899-0.960 CI95%) and at home (non-COPD: 0.731, 0.631-0.808 CI95%; COPD: 0.788, 0.678-0.864 CI95%). Regarding the area under the receiver operating characteristics curve (AUC), no statistically significant differences (p >0.01) between COPD and non-COPD groups were found in both settings, particularly for severe OSAS (AHI ≥30 events/h): 0.97 (0.92-0.99 CI95%) non-COPD vs. 0.98 (0.92-1.0 CI95%) COPD in the hospital, and 0.87 (0.79-0.92 CI95%) non-COPD vs. 0.86 (0.75-0.93 CI95%) COPD at home. CONCLUSION The agreement and the diagnostic performance of the estimated AHI from automated analysis of portable oximetry were similar regardless of the presence of COPD both in-lab and at-home. Particularly, portable oximetry could be used as an abbreviated screening test for moderate-to-severe OSAS in patients with COPD.
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Affiliation(s)
| | - Daniel Álvarez
- Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Andrea Crespo
- Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - C. Ainhoa Arroyo
- Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
| | | | | | - Roberto Hornero
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
| | - Félix del Campo
- Pneumology Service, Río Hortega University Hospital, Valladolid, Spain
- Biomedical Engineering Group, University of Valladolid, Valladolid, Spain
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13
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Multiscale Entropy Analysis of Unattended Oximetric Recordings to Assist in the Screening of Paediatric Sleep Apnoea at Home. ENTROPY 2017. [DOI: 10.3390/e19060284] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
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14
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Cundrle I, Belehrad M, Jelinek M, Olson LJ, Ludka O, Sramek V. The utility of perioperative polygraphy in the diagnosis of obstructive sleep apnea. Sleep Med 2016; 25:151-155. [DOI: 10.1016/j.sleep.2016.03.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Revised: 02/12/2016] [Accepted: 03/11/2016] [Indexed: 10/21/2022]
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15
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Marcos JV, Hornero R, Nabney IT, Álvarez D, Gutiérrez-Tobal GC, del Campo F. Regularity analysis of nocturnal oximetry recordings to assist in the diagnosis of sleep apnoea syndrome. Med Eng Phys 2015; 38:216-24. [PMID: 26719242 DOI: 10.1016/j.medengphy.2015.11.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2015] [Revised: 11/18/2015] [Accepted: 11/29/2015] [Indexed: 10/22/2022]
Abstract
The relationship between sleep apnoea-hypopnoea syndrome (SAHS) severity and the regularity of nocturnal oxygen saturation (SaO2) recordings was analysed. Three different methods were proposed to quantify regularity: approximate entropy (AEn), sample entropy (SEn) and kernel entropy (KEn). A total of 240 subjects suspected of suffering from SAHS took part in the study. They were randomly divided into a training set (96 subjects) and a test set (144 subjects) for the adjustment and assessment of the proposed methods, respectively. According to the measurements provided by AEn, SEn and KEn, higher irregularity of oximetry signals is associated with SAHS-positive patients. Receiver operating characteristic (ROC) and Pearson correlation analyses showed that KEn was the most reliable predictor of SAHS. It provided an area under the ROC curve of 0.91 in two-class classification of subjects as SAHS-negative or SAHS-positive. Moreover, KEn measurements from oximetry data exhibited a linear dependence on the apnoea-hypopnoea index, as shown by a correlation coefficient of 0.87. Therefore, these measurements could be used for the development of simplified diagnostic techniques in order to reduce the demand for polysomnographies. Furthermore, KEn represents a convincing alternative to AEn and SEn for the diagnostic analysis of noisy biomedical signals.
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Affiliation(s)
- J Víctor Marcos
- Grupo de Ingeniería Biomédica, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo de Belén 15, Valladolid 47011, Spain.
| | - Roberto Hornero
- Grupo de Ingeniería Biomédica, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo de Belén 15, Valladolid 47011, Spain.
| | - Ian T Nabney
- Non-linearity and Complexity Research Group, Aston University, Aston Triangle, Birmingham B4 7ET, United Kingdom.
| | - Daniel Álvarez
- Grupo de Ingeniería Biomédica, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo de Belén 15, Valladolid 47011, Spain.
| | - Gonzalo C Gutiérrez-Tobal
- Grupo de Ingeniería Biomédica, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Paseo de Belén 15, Valladolid 47011, Spain.
| | - Félix del Campo
- Hospital Universitario Pío del Río Hortega de Valladolid, Dulzaina 2, Valladolid 47013, Spain.
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Garde A, Karlen W, Dehkordi P, Ansermino JM, Dumont GA. Oxygen saturation resolution influences regularity measurements. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2015; 2014:2257-60. [PMID: 25570437 DOI: 10.1109/embc.2014.6944069] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
The measurement of regularity in the oxygen saturation (SpO(2)) signal has been suggested for use in identifying subjects with sleep disordered breathing (SDB). Previous work has shown that children with SDB have lower SpO(2) regularity than subjects without SDB (NonSDB). Regularity was measured using non-linear methods like approximate entropy (ApEn), sample entropy (SamEn) and Lempel-Ziv (LZ) complexity. Different manufacturer's pulse oximeters provide SpO(2) at various resolutions and the effect of this resolution difference on SpO(2) regularity, has not been studied. To investigate this effect, we used the SpO(2) signal of children with and without SDB, recorded from the Phone Oximeter (0.1% resolution) and the same SpO(2) signal rounded to the nearest integer (artificial 1% resolution). To further validate the effect of rounding, we also used the SpO(2) signal (1% resolution) recorded simultaneously from polysomnography (PSG), as a control signal. We estimated SpO(2) regularity by computing the ApEn, SamEn and LZ complexity, using a 5-min sliding window and showed that different resolutions provided significantly different results. The regularity calculated using 0.1% SpO(2) resolution provided no significant differences between SDB and NonSDB. However, the artificial 1% resolution SpO(2) provided significant differences between SDB and NonSDB, showing a more random SpO(2) pattern (lower SpO(2) regularity) in SDB children, as suggested in the past. Similar results were obtained with the SpO(2) recorded from PSG (1% resolution), which further validated that this SpO(2) regularity change was due to the rounding effect. Therefore, the SpO(2) resolution has a great influence in regularity measurements like ApEn, SamEn and LZ complexity that should be considered when studying the SpO(2) pattern in children with SDB.
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Alvarez-Estevez D, Moret-Bonillo V. Computer-Assisted Diagnosis of the Sleep Apnea-Hypopnea Syndrome: A Review. SLEEP DISORDERS 2015; 2015:237878. [PMID: 26266052 PMCID: PMC4523666 DOI: 10.1155/2015/237878] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/01/2015] [Revised: 06/15/2015] [Accepted: 06/21/2015] [Indexed: 02/07/2023]
Abstract
Automatic diagnosis of the Sleep Apnea-Hypopnea Syndrome (SAHS) has become an important area of research due to the growing interest in the field of sleep medicine and the costs associated with its manual diagnosis. The increment and heterogeneity of the different techniques, however, make it somewhat difficult to adequately follow the recent developments. A literature review within the area of computer-assisted diagnosis of SAHS has been performed comprising the last 15 years of research in the field. Screening approaches, methods for the detection and classification of respiratory events, comprehensive diagnostic systems, and an outline of current commercial approaches are reviewed. An overview of the different methods is presented together with validation analysis and critical discussion of the current state of the art.
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Affiliation(s)
| | - Vicente Moret-Bonillo
- Laboratory for Research and Development in Artificial Intelligence (LIDIA), Department of Computer Science, University of A Coruña, 15071 A Coruña, Spain
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18
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Probabilistic neural network approach for the detection of SAHS from overnight pulse oximetry. Med Biol Eng Comput 2012; 51:305-15. [PMID: 23160897 DOI: 10.1007/s11517-012-0995-4] [Citation(s) in RCA: 23] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2012] [Accepted: 11/03/2012] [Indexed: 10/27/2022]
Abstract
Diagnosis of sleep apnea hypopnoea syndrome (SAHS) depends on the apnea-hypopnea index determined by the standard in-laboratory overnight polysomnography (PSG). PSG is a costly, labor intensive and, at times, inaccessible approach. Because of the high demand, the need for timely diagnosis and the associated costs, novel methods for SAHS detection are required. In this study, a novel multivariate system is proposed for SAHS detection from the analysis of overnight blood oxygen saturation (SpO2). 115 subjects with SAHS suspicion were studied. A starting set of 17 time domain, stochastic, frequency-domain and nonlinear features were initially computed from SpO2 recordings. Sequential forward feature selection and a probabilistic neural network with leave-one-out cross-validation were applied. Oxygen desaturations below a 4 % threshold within 30 s (ODI430), restorations of 4 % within 10 s (RES4), median value (Sat50), SD1 Poincaré descriptor and the relative power in the 0.013-0.067 Hz frequency band (PSD15/75) formed the optimum features subset. 92.4 % sensitivity and 95.9 % specificity were achieved. Results significantly outperformed the univariate and multivariate approaches reported in literature. The outcome is a simple cost-effective tool that could be used as an alternative or supplementary method in a domiciliary approach to early diagnosis of SAHS.
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Pulse oximetry for the detection of obstructive sleep apnea syndrome: can the memory capacity of oxygen saturation influence their diagnostic accuracy? SLEEP DISORDERS 2011; 2011:427028. [PMID: 23471171 PMCID: PMC3581239 DOI: 10.1155/2011/427028] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2011] [Revised: 06/20/2011] [Accepted: 07/05/2011] [Indexed: 11/17/2022]
Abstract
Objective. To assess the diagnostic ability of WristOx 3100 using its three different recording settings in patients with suspected obstructive sleep apnea syndrome (OSAS). Methods. All participants (135) performed the oximetry (three oximeters WristOx 3100) and polysomnography (PSG) simultaneously in the sleep laboratory. Both recordings were interpreted blindly. Each oximeter was set to one of three different recording settings (memory capabilities 0.25, 0.5, and 1 Hz). The software (nVision 5.1) calculated the adjusted O2 desaturation index-mean number of O2 desaturation per hour of analyzed recording ≥2, 3, and 4% (ADI2, 3, and 4). The ADI2, 3, and 4 cutoff points that better discriminated between subjects with or without OSAS arose from the receiver-operator characteristics (ROCs) curve analysis. OSAS was defined as a respiratory disturbance index (RDI) ≥ 5. Results. 101 patients were included (77 men, mean age 52, median RDI 22.6, median BMI 27.4 kg/m2). The area under the ROCs curves (AUC-ROCs) of ADI2, 3, and 4 with different data storage rates were similar (AUC-ROCs with data storage rates of 0.25/0.5/1 Hz: ADI2: 0.958/0.948/0.965, ADI3: 0.961/0.95/0.966, and ADI4: 0.957/0.949/0.963, P NS). Conclusions. The ability of WristOx 3100 to detect patients with OSAS was not affected by the data storage rate of the oxygen saturation signal. Both memory capacity of 0.25, 0.5, or 1 Hz showed a similar performance for the diagnosis of OSAS.
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Maurer JT. Early diagnosis of sleep related breathing disorders. GMS CURRENT TOPICS IN OTORHINOLARYNGOLOGY, HEAD AND NECK SURGERY 2010; 7:Doc03. [PMID: 22073090 PMCID: PMC3199834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
Obstructive sleep apnea (OSA) being the most frequent sleep related breathing disorder results in non-restorative sleep, an increased cardiovascular morbidity and mortality as well as an elevated number of accidents. In Germany at least two million people have to be expected. If obstructive sleep apnea is diagnosed early enough then sleep may regain its restorative function, daytime performance may be improved and accident risk as well as cardiovascular risk may be normalised. This review critically evaluates anamnestic parameters, questionnaires, clinical findings and unattended recordings during sleep regarding their diagnostic accurracy in recognising OSA. There are numerous tools with insufficient results or too few data disqualifying them for screening for OSA. Promising preliminary results are published concerning neural network analysis of a high number of clinical parameters and non-linear analysis of oximetry itself or in combination with heart rate. Nasal pressure recordings can be used for risk estimation even without expertise in sleep medicine. More data is needed. Unattended portable monitoring used by qualified physicians is the gold standard procedure when screening methods for OSA are compared. It has a very high sensitivity and specificity well documented by several meta-analyses.
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Affiliation(s)
- Joachim T. Maurer
- Sleep Disorders Centre, University Dept. of Otorhinolaryngology, Head and Neck Surgery Mannheim, Medical Faculty Mannheim of the Ruprecht-Karls-University Heidelberg, Mannheim, Germany,*To whom correspondence should be addressed: Joachim T. Maurer, Sleep Disorders Centre, University Dept. of Otorhinolaryngology, Head and Neck Surgery Mannheim, Medical Faculty Mannheim of the Ruprecht-Karls-University Heidelberg, 68135 Mannheim, Germany, Telephone: +49 (0)621 383 1600, Telefax: +49 (0)621 383 1972, E-mail:
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Alvarez D, Hornero R, Marcos JV, del Campo F. Multivariate analysis of blood oxygen saturation recordings in obstructive sleep apnea diagnosis. IEEE Trans Biomed Eng 2010; 57:2816-24. [PMID: 20624698 DOI: 10.1109/tbme.2010.2056924] [Citation(s) in RCA: 91] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This study focuses on the analysis of blood oxygen saturation (SaO(2)) from nocturnal pulse oximetry (NPO) to help in the diagnosis of the obstructive sleep apnea (OSA) syndrome. A population of 148 patients suspected of suffering from OSA syndrome was studied. A wide set of 16 features was used to characterize changes in the SaO(2) profile during the night. Our feature set included common statistics in the time and frequency domains, conventional spectral characteristics from the power spectral density (PSD) function, and nonlinear features. We performed feature selection by means of a step-forward logistic regression (LR) approach with leave-one-out cross-validation. Second- and fourth-order statistical moments in the time domain (M2t and M4t), the relative power in the 0.014-0.033 Hz frequency band ( P(R)), and the Lempel-Ziv complexity (LZC) were automatically selected. 92.0% sensitivity, 85.4% specificity, and 89.7% accuracy were obtained. The optimum feature set significantly improved the diagnostic ability of each feature individually. Furthermore, our results outperformed classic oximetric indexes commonly used by physicians. We conclude that simultaneous analysis in the time and frequency domains by means of statistical moments, spectral and nonlinear features could provide complementary information from NPO to improve OSA diagnosis.
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Affiliation(s)
- Daniel Alvarez
- ETSI Telecomunicación, University of Valladolid, Valladolid, Spain.
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22
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Automated detection of obstructive sleep apnoea syndrome from oxygen saturation recordings using linear discriminant analysis. Med Biol Eng Comput 2010; 48:895-902. [DOI: 10.1007/s11517-010-0646-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2009] [Accepted: 05/30/2010] [Indexed: 10/19/2022]
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23
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Félix del Campo Matía, Hornero Sánchez R, Zamarrón Sanz C, Álvarez González D, Víctor Marcos Martín J. Variabilidad de la señal de frecuencia de pulso obtenida mediante pulsioximetría nocturna en pacientes con síndrome de apnea hipopnea del sueño. Arch Bronconeumol 2010; 46:116-21. [DOI: 10.1016/j.arbres.2009.11.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2009] [Revised: 10/25/2009] [Accepted: 11/16/2009] [Indexed: 11/26/2022]
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Marcos JV, Hornero R, Álvarez D, Nabney IT, del Campo F, Zamarrón C. The classification of oximetry signals using Bayesian neural networks to assist in the detection of obstructive sleep apnoea syndrome. Physiol Meas 2010; 31:375-94. [DOI: 10.1088/0967-3334/31/3/007] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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Abstract
Patients suspected of suffering sleep apnea and hypopnea syndrome (SAHS) have to undergo sleep studies such as expensive polysomnographies to be diagnosed. Healthcare professionals are constantly looking for ways to improve the ease of diagnosis and comfort for this kind of patients as well as reducing both the number of sleep studies they need to undergo and the waiting times. Relating to this scenario, some research proposals and commercial products are appearing, but all of them record the physiological data of patients to portable devices and, in the morning, these data are loaded into hospital computers where physicians analyze them by making use of specialized software. In this paper, we present an alternative proposal that promotes not only a transmission of physiological data but also a real-time analysis of these data locally at a mobile device. For that, we have built a classifier that provides an accuracy of 93% and a receiver operating characteristic-area under the curve (ROC-AUC) of 98.5% on SpO(2) signals available in the annotated Apnea-ECG Database. This local analysis allows the detection of anomalous situations as soon as they are generated. The classifier has been implemented taking into consideration the restricted resources of mobile devices.
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Affiliation(s)
- Alfredo Burgos
- University of the Basque Country, Guipuzcoa 20018, Spain.
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26
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Álvarez D, Hornero R, Abásolo D, del Campo F, Zamarrón C, López M. Nonlinear measure of synchrony between blood oxygen saturation and heart rate from nocturnal pulse oximetry in obstructive sleep apnoea syndrome. Physiol Meas 2009; 30:967-82. [DOI: 10.1088/0967-3334/30/9/008] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Marcos JV, Hornero R, Alvarez D, del Campo F, Zamarrón C. Assessment of four statistical pattern recognition techniques to assist in obstructive sleep apnoea diagnosis from nocturnal oximetry. Med Eng Phys 2009; 31:971-8. [PMID: 19592290 DOI: 10.1016/j.medengphy.2009.05.010] [Citation(s) in RCA: 55] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2009] [Accepted: 05/31/2009] [Indexed: 11/28/2022]
Abstract
The aim of this study is to assess the utility of traditional statistical pattern recognition techniques to help in obstructive sleep apnoea (OSA) diagnosis. Classifiers based on quadratic (QDA) and linear (LDA) discriminant analysis, K-nearest neighbours (KNN) and logistic regression (LR) were evaluated. Spectral and nonlinear input features from oxygen saturation (SaO(2)) signals were applied. A total of 187 recordings from patients suspected of suffering from OSA were available. This initial dataset was divided into training set (74 subjects) and test set (113 subjects). Twelve classification algorithms were developed by applying QDA, LDA, KNN and LR with spectral features, nonlinear features and combination of both groups. The performance of each algorithm was measured on the test set by means of classification accuracy and receiver operating characteristic (ROC) analysis. QDA, LDA and LR showed better classification capability than KNN. The classifier based on LDA with spectral features provided the best diagnostic ability with an accuracy of 87.61% (91.05% sensitivity and 82.61% specificity) and an area under the ROC curve (AROC) of 0.925. The proposed statistical pattern recognition techniques could be applied as an OSA screening tool.
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Affiliation(s)
- J Víctor Marcos
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, Universidad de Valladolid, Camino del Cementerio s/n, 47011 Valladolid, Spain.
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Álvarez-Estévez D, Moret-Bonillo V. Fuzzy reasoning used to detect apneic events in the sleep apnea-hypopnea syndrome. EXPERT SYSTEMS WITH APPLICATIONS 2009. [DOI: 10.1016/j.eswa.2008.11.043] [Citation(s) in RCA: 28] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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Polat K, Yosunkaya S, Güneş S. A new approach to diagnosing of importance degree of obstructive sleep apnea syndrome: Pairwise AIRS and Fuzzy-AIRS classifiers. J Med Syst 2009; 32:489-97. [PMID: 19058653 DOI: 10.1007/s10916-008-9155-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Artificial Immune Recognition System (AIRS) classifier algorithm is robust and effective in medical dataset classification applications such as breast cancer, heart disease, diabetes diagnosis etc. In our previous work, we have proposed a new resource allocation mechanism called fuzzy resource allocation in AIRS algorithm both to improve the classification accuracy and to decrease the computation time in classification process. Here, AIRS and Fuzzy-AIRS classifier algorithms and one against all approach have been combined to increase the classification accuracy of obstructive sleep apnea syndrome (OSAS) that is an important disease that influences both the right and the left cardiac ventricle. The OSAS dataset consists of four classes including of normal (25 subjects), mild OSAS (AHI (Apnea and Hypoapnea Index) = 5-15 and 14 subjects), moderate OSAS (AHI < 15-30 and 18 subjects), and serious OSAS (AHI > 30 and 26 subjects). In the extracting of features that is characterized the OSAS disease, the clinical features obtained from Polysomnography used diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering from this disease have been used. The used clinical features are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. Even though AIRS and Fuzzy-AIRS classifiers have been used in the classifying multi-class problems, theirs classification performances are low in the case of multi-class classification problems. Therefore, we have used two classes in AIRS and Fuzzy-AIRS classifiers by means of one against all approach instead of four classes comprising the healthy subjects, mild OSAS, moderate OSAS, and serious OSAS. We have applied the AIRS, Fuzzy-AIRS, AIRS with one against all approach (Pairwise AIRS), and Fuzzy-AIRS with one against all approach (Pairwise Fuzzy-AIRS) to OSAS dataset. The obtained classification accuracies are 63.41%, 63.41%, 87.19%, and 84.14% using the above methods for 200 resources, respectively. These results show that the best method for diagnosis of OSAS is the combination of AIRS and one against all approach (Pairwise AIRS).
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Affiliation(s)
- Kemal Polat
- Department of Electrical & Electronics Engineering, Selcuk University, 42075 Konya, Turkey.
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Polat K, Yosunkaya S, Güneş S. Pairwise ANFIS approach to determining the disorder degree of obstructive sleep apnea syndrome. J Med Syst 2009; 32:379-87. [PMID: 18814494 DOI: 10.1007/s10916-008-9143-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Obstructive sleep apnea syndrome (OSAS) is an important disease that affects both the right and the left cardiac ventricle. This paper presents a novel classification method called pairwise ANFIS based on Adaptive Neuro-Fuzzy Inference System (ANFIS) and one against all method for detecting the obstructive sleep apnea syndrome. In order to extract the features related with OSAS, we have used the clinical features obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea (OSA) in patients clinically suspected of suffering from this disease. The clinical features obtained from Polysomnography Reports are Arousals Index (ARI), Apnea and Hypoapnea Index (AHI), SaO2 minimum value in stage of REM, and Percent Sleep Time (PST) in stage of SaO2 intervals bigger than 89%. Since ANFIS has output with one class, we have extended the output of ANFIS to multi class by means of one against all method to diagnose the OSAS that has four classes consisting of normal (25 subjects), mild OSAS (AHI=5-15 and 14 subjects), middle OSAS (AHI<15-30 and 18 subjects), and heavy OSAS (AHI>30 and 26 subjects). The classification accuracy, sensitivity and specifity analysis, mean square error, and confusion matrix have been used to test the performance of proposed method. The obtained classification accuracies are 82.92%, 82.92%, 85.36%, and 87.80% for each class including normal, mild OSAS, middle OSAS, and heavy OSAS using ANFIS with one against all method with 50-50% train-test split, respectively. Combining ANFIS and one against all method that is firstly proposed by us was firstly applied for diagnosing the OSAS. The proposed method has produced very promising results in the detecting the obstructive sleep apnea syndrome.
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Affiliation(s)
- Kemal Polat
- Electrical and Electronics Engineering Department, Selcuk University, 42035 Konya, Turkey.
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Nigro CA, Aimaretti S, Gonzalez S, Rhodius E. Validation of the WristOx 3100 oximeter for the diagnosis of sleep apnea/hypopnea syndrome. Sleep Breath 2008; 13:127-36. [PMID: 18830731 DOI: 10.1007/s11325-008-0217-3] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2008] [Revised: 08/03/2008] [Accepted: 08/04/2008] [Indexed: 11/28/2022]
Abstract
OBJECTIVE To evaluate the diagnostic accuracy of the Nonin WristOx 3100 and its software (nVision 5.0) in patients with suspicion of sleep apnea/hypopnea syndrome (SAHS). METHODS All participants (168) had the oximetry and polysomnography simultaneously. The two recordings were interpreted blindly. The software calculated: adjusted O(2) desaturation index [ADI]-mean number of O(2) desaturation per hour of total recording analyzed time of > or = 2%, 3%, 4%, 5%, and 6% (ADI2, 3, 4, 5, and 6) and AT90-accumulated time at SO(2) < 90%. The ADI2, 3, 4, 5, and 6 and the AT90 cutoff points that better discriminated between subjects with or without SAHS arose from the receiver operating characteristic curve analysis. The sensitivity (S), specificity (E), and positive and negative likelihood ratio (LR+, LR-) for the different thresholds for ADI were calculated. RESULTS One hundred and fifty-four patients were included (119 men, mean age 51, median apnea/hypopnea index [AHI] 14, median body mass index [BMI] 28.3 kg/m(2)). The best cutoff points of ADI were: SAHS = AHI > or = 5: ADI2 > 19.3 (S 89%, E 94%, LR+ 15.5 LR- 0.11); SAHS =AHI > or = 10: ADI3 > 10.5 (S 88%, E 94%, LR+ 15 LR- 0.12); SAHS = AHI > or = 15: ADI3 > 13.4 (S 88%, E 90%, LR+ 8.9, LR- 0.14). AT90 had the lowest diagnosis accuracy. An ADI2 < or = 12.2 excluded SAHS (AHI > or = 5 and 10; S 100%, LR- 0) and ADI3 > 4.3 (AHI > or = 5 and 10) or 32 (AHI > or = 15) confirmed SAHS (E 100%). CONCLUSIONS A negative oximetry defined as ADI2 < or = 12.2 excluded SAHS defined as AHI > or = 5 or 10 with a sensitivity and negative likelihood ratio of 100% and 0%, respectively. Furthermore, a positive oximetry defined as an ADI3 > 32 (SAHS = AHI > or = 15) had a specificity of 100% to confirm the pathology.
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Marcos JV, Hornero R, Alvarez D, Del Campo F, Zamarrón C, López M. Utility of multilayer perceptron neural network classifiers in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2008; 92:79-89. [PMID: 18672313 DOI: 10.1016/j.cmpb.2008.05.006] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2007] [Revised: 05/20/2008] [Accepted: 05/24/2008] [Indexed: 05/26/2023]
Abstract
The aim of this study is to assess the ability of multilayer perceptron (MLP) neural networks as an assistant tool in the diagnosis of the obstructive sleep apnoea syndrome (OSAS). Non-linear features from nocturnal oxygen saturation (SaO(2)) recordings were used to discriminate between OSAS positive and negative patients. A total of 187 subjects suspected of suffering from OSAS (111 with a positive diagnosis of OSAS and 76 with a negative diagnosis of OSAS) took part in the study. The initial population was divided into training, validation and test sets for deriving and testing our neural network classifier. Three methods were applied to extract non-linear features from SaO(2) signals: approximate entropy (ApEn), central tendency measure (CTM) and Lempel-Ziv complexity (LZC). The selected MLP-based classifier provided a diagnostic accuracy of 85.5% (89.8% sensitivity and 79.4% specificity). Our neural network algorithm could represent a useful technique for OSAS detection. It could contribute to reduce the demand for polysomnographic studies in OSAS screening.
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Affiliation(s)
- J Víctor Marcos
- Biomedical Engineering Group, E.T.S. Ingenieros de Telecomunicación, University of Valladolid, Spain.
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Polat K, Yosunkaya S, Güneş S. Comparison of different classifier algorithms on the automated detection of obstructive sleep apnea syndrome. J Med Syst 2008; 32:243-50. [PMID: 18444362 DOI: 10.1007/s10916-008-9129-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
In this paper, we have compared the classifier algorithms including C4.5 decision tree, le artificial neural network (ANN), artificial immune recognition system (AIRS), and adaptive neuro-fuzzy inference system (ANFIS) in the diagnosis of obstructive sleep apnea syndrome (OSAS), which is an important disease that affects both the right and the left cardiac ventricle. The goal of this study was to find the best classifier model on the diagnosis of OSAS. The clinical features were obtained from Polysomnography device as a diagnostic tool for obstructive sleep apnea in patients clinically suspected of suffering this disease in this study. The clinical features are arousals index, apneahypopnea index (AHI), SaO2 minimum value in stage of rapid eye movement, and percent sleep time in stage of SaO2 intervals bigger than 89%. In our experiments, a total of 83 patients (58 with a positive OSAS (AHI>5) and 25 with a negative OSAS such that normal subjects) were examined. The decision support systems can help to physicians in the diagnosing of any disorder or disease using clues obtained from signal or images taken from subject having any disorder. In order to compare the used classifier algorithms, the mean square error, classification accuracy, area under the receiver operating characteristics curve (AUC), and sensitivity and specificity analysis have been used. The obtained AUC values of C4.5 decision tree, ANN, AIRS, and ANFIS classifiers are 0.971, 0.96, 0.96, and 0.922, respectively. These results have shown that the best classifier system is C4.5 decision tree classifier on the diagnosis of obstructive sleep apnea syndrome.
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Affiliation(s)
- Kemal Polat
- Electrical and Electronics Engineering Department, Selcuk University, 42035 Konya, Turkey.
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Weinreich G, Armitstead J, Teschler H. Pattern recognition of obstructive sleep apnoea and Cheyne-Stokes respiration. Physiol Meas 2008; 29:869-78. [PMID: 18603668 DOI: 10.1088/0967-3334/29/8/002] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
The aim of this study was to assess the validity of an artificial neural network based on flow-related spectral entropy as a diagnostic test for obstructive sleep apnoea and Cheyne-Stokes respiration. A data set of 37 subjects was used for spectral analysis of the airflow by performing a fast Fourier transform. The examined intervals were divided into epochs of 3 min. Spectral entropy S was applied as a measure for the spread of the related power spectrum. The spectrum was divided into several frequency areas with various subsets of spectral entropy. We studied 11 subjects with obstructive apnoeas (n = 267 epochs), 10 subjects with obstructive hypopnoeas (n = 80 epochs), 11 subjects with Cheyne-Stokes respiration (n = 253 epochs) and 5 subjects with normal breathing in non-REM sleep (n = 174 epochs). Based on spectral entropy an artificial neural network was built, and we obtained a sensitivity of 90.2% and a specificity of 90.9% for distinguishing between obstructive apnoeas and Cheyne-Stokes respiration, and a sensitivity of 91.3% and a specificity of 94.6% for discriminating between obstructive hypopnoeas and normal breathing in non-REM sleep. This resulted in an accuracy of 91.5% for identifying flow patterns of obstructive sleep apnoea, Cheyne-Stokes respiration and normal breathing in non-REM sleep. It is concluded that the use of an artificial neural network relying on spectral analysis of the airflow could be a useful method as a diagnostic test for obstructive sleep apnoea and Cheyne-Stokes respiration.
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Affiliation(s)
- Gerhard Weinreich
- Ruhrlandklinik, Department of Pneumology, University Hospital, Tüschener Weg 40, D-45239 Essen, Germany.
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Virkki A, Polo O, Saaresranta T, Laapotti-Salo A, Gyllenberg M, Aittokallio T. Overnight features of transcutaneous carbon dioxide measurement as predictors of metabolic status. Artif Intell Med 2008; 42:55-65. [DOI: 10.1016/j.artmed.2007.09.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2006] [Revised: 09/21/2007] [Accepted: 09/21/2007] [Indexed: 01/04/2023]
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Alvarez D, Hornero R, García M, del Campo F, Zamarrón C. Improving diagnostic ability of blood oxygen saturation from overnight pulse oximetry in obstructive sleep apnea detection by means of central tendency measure. Artif Intell Med 2007; 41:13-24. [PMID: 17643971 DOI: 10.1016/j.artmed.2007.06.002] [Citation(s) in RCA: 62] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2006] [Revised: 05/29/2007] [Accepted: 06/12/2007] [Indexed: 11/22/2022]
Abstract
OBJECTIVES Nocturnal pulse oximetry is a widely used alternative to polysomnography (PSG) in screening for obstructive sleep apnea (OSA) syndrome. Several oximetric indexes have been derived from nocturnal blood oxygen saturation (SaO2). However, they suffer from several limitations. The present study is focused on the usefulness of nonlinear methods in deriving new measures from oximetry signals to improve the diagnostic accuracy of classical oximetric indexes. Specifically, we assessed the validity of central tendency measure (CTM) as a screening test for OSA in patients clinically suspected of suffering from this disease. MATERIALS AND METHODS We studied 187 subjects suspected of suffering from OSA referred to the sleep unit. A nocturnal pulse oximetry study was applied simultaneously to a conventional PSG. Three different index groups were compared. The first one was composed by classical indexes provided by our oximeter: oxygen desaturation indexes (ODIs) and cumulative time spent below a saturation of 90% (CT90). The second one was formed by indexes derived from a nonlinear method previously studied by our group: approximate entropy (ApEn). The last one was composed by indexes derived from a CTM analysis. RESULTS For a radius in the scatter plot equal to 1, CTM values corresponding to OSA positive patients (0.30+/-0.20, mean+/-S.D.) were significantly lower (p<<0.001) than those values from OSA negative subjects (0.71+/-0.18, mean+/-S.D.). CTM was significantly correlated with classical indexes and indexes from ApEn analysis. CTM provided the highest correlation with the apnea-hipopnea index AHI (r=-0.74, p<0.0001). Moreover, it reached the best results from the receiver operating characteristics (ROC) curve analysis, with 90.1% sensitivity, 82.9% specificity, 88.5% positive predictive value, 85.1% negative predictive value, 87.2% accuracy and an area under the ROC curve of 0.924. Finally, the AHI derived from the quadratic regression curve for the CTM showed better agreement with the AHI from PSG than classical and ApEn derived indexes. CONCLUSION The results suggest that CTM could improve the diagnostic ability of SaO2 signals recorded from portable monitoring. CTM could be a useful tool for physicians in the diagnosis of OSA syndrome.
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Affiliation(s)
- Daniel Alvarez
- E.T.S.I. de Telecomunicación, University of Valladolid, and Hospital del Río Hortega, Servicio de Neumología, Valladolid, Spain.
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Choi SJ. Sleep Breathing Disorder. Tuberc Respir Dis (Seoul) 2007. [DOI: 10.4046/trd.2007.63.1.5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Affiliation(s)
- Soo Jeon Choi
- Division of Respirology, Department of Medicine, Inje University Sanggye Paik Hospital, Seoul, Korea
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