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  1. Article ; Online: Deep learning for obstructive sleep apnea diagnosis based on single channel oximetry.

    Levy, Jeremy / Álvarez, Daniel / Del Campo, Félix / Behar, Joachim A

    Nature communications

    2023  Volume 14, Issue 1, Page(s) 4881

    Abstract: Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagnosis remains a challenge. Existing home sleep tests may provide acceptable diagnosis performance but have shown several limitations. In this retrospective ... ...

    Abstract Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagnosis remains a challenge. Existing home sleep tests may provide acceptable diagnosis performance but have shown several limitations. In this retrospective study, we used 12,923 polysomnography recordings from six independent databases to develop and evaluate a deep learning model, called OxiNet, for the estimation of the apnea-hypopnea index from the oximetry signal. We evaluated OxiNet performance across ethnicity, age, sex, and comorbidity. OxiNet missed 0.2% of all test set moderate-to-severe OSA patients against 21% for the best benchmark.
    MeSH term(s) Humans ; Retrospective Studies ; Deep Learning ; Sleep Apnea, Obstructive/diagnosis ; Oximetry ; Comorbidity
    Language English
    Publishing date 2023-08-12
    Publishing country England
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ZDB-ID 2553671-0
    ISSN 2041-1723 ; 2041-1723
    ISSN (online) 2041-1723
    ISSN 2041-1723
    DOI 10.1038/s41467-023-40604-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article: Diagnosis of Obstructive Sleep Apnea in Patients with Associated Comorbidity.

    Del Campo, Félix / Arroyo, C Ainhoa / Zamarrón, Carlos / Álvarez, Daniel

    Advances in experimental medicine and biology

    2022  Volume 1384, Page(s) 43–61

    Abstract: Obstructive sleep apnea (OSA) is a heterogeneous disease with many physiological implications. OSA is associated with a great diversity of diseases, with which it shares common and very often bidirectional pathophysiological mechanisms, leading to ... ...

    Abstract Obstructive sleep apnea (OSA) is a heterogeneous disease with many physiological implications. OSA is associated with a great diversity of diseases, with which it shares common and very often bidirectional pathophysiological mechanisms, leading to significantly negative implications on morbidity and mortality. In these patients, underdiagnosis of OSA is high. Concerning cardiorespiratory comorbidities, several studies have assessed the usefulness of simplified screening tests for OSA in patients with hypertension, COPD, heart failure, atrial fibrillation, stroke, morbid obesity, and in hospitalized elders.The key question is whether there is any benefit in the screening for the existence of OSA in patients with comorbidities. In this regard, there are few studies evaluating the performance of the various diagnostic procedures in patients at high risk for OSA. The purpose of this chapter is to review the existing literature about diagnosis in those diseases with a high risk for OSA, with special reference to artificial intelligence-related methods.
    MeSH term(s) Aged ; Artificial Intelligence ; Atrial Fibrillation/complications ; Comorbidity ; Humans ; Polysomnography/methods ; Sleep Apnea, Obstructive/diagnosis ; Sleep Apnea, Obstructive/epidemiology
    Language English
    Publishing date 2022-10-22
    Publishing country United States
    Document type Journal Article ; Review
    ISSN 2214-8019 ; 0065-2598
    ISSN (online) 2214-8019
    ISSN 0065-2598
    DOI 10.1007/978-3-031-06413-5_4
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  3. Article ; Online: Machine learning for nocturnal diagnosis of chronic obstructive pulmonary disease using digital oximetry biomarkers.

    Levy, Jeremy / Álvarez, Daniel / Del Campo, Felix / Behar, Joachim A

    Physiological measurement

    2021  Volume 42, Issue 5

    Abstract: Objective. ...

    Abstract Objective.
    MeSH term(s) Biomarkers ; Humans ; Machine Learning ; Oximetry ; Polysomnography ; Pulmonary Disease, Chronic Obstructive/diagnosis
    Chemical Substances Biomarkers
    Language English
    Publishing date 2021-06-17
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1149545-5
    ISSN 1361-6579 ; 0967-3334
    ISSN (online) 1361-6579
    ISSN 0967-3334
    DOI 10.1088/1361-6579/abf5ad
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1559: Show issues Location:
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  4. Article ; Online: Influencia de PCR SARS-CoV-2 positivas en los ingresos hospitalarios por COVID-19 en un área de salud española.

    López-Izquierdo, Raúl / Del Campo, Félix / Eiros, José María

    Medicina clinica

    2021  Volume 156, Issue 8, Page(s) 407–408

    Title translation Influence of positive SARS-CoV-2 CRP on hospital admissions for COVID-19 in a Spanish health area.
    MeSH term(s) COVID-19/diagnosis ; COVID-19 Nucleic Acid Testing ; Hospitalization/trends ; Hospitals ; Humans ; Polymerase Chain Reaction ; Risk Factors ; Spain
    Language Spanish
    Publishing date 2021-01-29
    Publishing country Spain
    Document type Letter ; Research Support, Non-U.S. Gov't
    ZDB-ID 411607-0
    ISSN 1578-8989 ; 0025-7753
    ISSN (online) 1578-8989
    ISSN 0025-7753
    DOI 10.1016/j.medcli.2020.12.009
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  5. Article ; Online: Influence of positive SARS-CoV-2 CRP on hospital admissions for COVID-19 in a Spanish health area.

    López-Izquierdo, Raúl / Del Campo, Félix / Eiros, José María

    Medicina clinica (English ed.)

    2021  Volume 156, Issue 8, Page(s) 407–408

    Language English
    Publishing date 2021-03-19
    Publishing country Spain
    Document type Case Reports
    ISSN 2387-0206
    ISSN (online) 2387-0206
    DOI 10.1016/j.medcle.2020.12.012
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: From sleep medicine to medicine during sleep.

    Behar, Joachim A / Shamay, Yosi / Álvarez, Daniel / Del Campo, Félix / Penzel, Thomas

    Physiological measurement

    2021  Volume 42, Issue 12

    MeSH term(s) Humans ; Sleep ; Sleep Wake Disorders
    Language English
    Publishing date 2021-12-29
    Publishing country England
    Document type Journal Article
    ZDB-ID 1149545-5
    ISSN 1361-6579 ; 0967-3334
    ISSN (online) 1361-6579
    ISSN 0967-3334
    DOI 10.1088/1361-6579/ac3e38
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1559: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
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    ab Jg. 2022: Lesesaal (EG)

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  7. Article ; Online: Digital oximetry biomarkers for assessing respiratory function: standards of measurement, physiological interpretation, and clinical use.

    Levy, Jeremy / Álvarez, Daniel / Rosenberg, Aviv A / Alexandrovich, Alexandra / Del Campo, Félix / Behar, Joachim A

    NPJ digital medicine

    2021  Volume 4, Issue 1, Page(s) 1

    Abstract: Pulse oximetry is routinely used to non-invasively monitor oxygen saturation levels. A low oxygen level in the blood means low oxygen in the tissues, which can ultimately lead to organ failure. Yet, contrary to heart rate variability measures, a field ... ...

    Abstract Pulse oximetry is routinely used to non-invasively monitor oxygen saturation levels. A low oxygen level in the blood means low oxygen in the tissues, which can ultimately lead to organ failure. Yet, contrary to heart rate variability measures, a field which has seen the development of stable standards and advanced toolboxes and software, no such standards and open tools exist for continuous oxygen saturation time series variability analysis. The primary objective of this research was to identify, implement and validate key digital oximetry biomarkers (OBMs) for the purpose of creating a standard and associated reference toolbox for continuous oximetry time series analysis. We review the sleep medicine literature to identify clinically relevant OBMs. We implement these biomarkers and demonstrate their clinical value within the context of obstructive sleep apnea (OSA) diagnosis on a total of n = 3806 individual polysomnography recordings totaling 26,686 h of continuous data. A total of 44 digital oximetry biomarkers were implemented. Reference ranges for each biomarker are provided for individuals with mild, moderate, and severe OSA and for non-OSA recordings. Linear regression analysis between biomarkers and the apnea hypopnea index (AHI) showed a high correlation, which reached [Formula: see text]. The resulting python OBM toolbox, denoted "pobm", was contributed to the open software PhysioZoo ( physiozoo.org ). Studying the variability of the continuous oxygen saturation time series using pbom may provide information on the underlying physiological control systems and enhance our understanding of the manifestations and etiology of diseases, with emphasis on respiratory diseases.
    Language English
    Publishing date 2021-01-04
    Publishing country England
    Document type Journal Article
    ISSN 2398-6352
    ISSN (online) 2398-6352
    DOI 10.1038/s41746-020-00373-5
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  8. Article: Oximetry Indices in the Management of Sleep Apnea: From Overnight Minimum Saturation to the Novel Hypoxemia Measures.

    Álvarez, Daniel / Gutiérrez-Tobal, Gonzalo C / Vaquerizo-Villar, Fernando / Moreno, Fernando / Del Campo, Félix / Hornero, Roberto

    Advances in experimental medicine and biology

    2022  Volume 1384, Page(s) 219–239

    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 ... ...

    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 (SpO
    MeSH term(s) Humans ; Hypoxia/diagnosis ; Oximetry/methods ; Oxygen ; Sleep Apnea Syndromes/diagnosis ; Sleep Apnea Syndromes/therapy ; Sleep Apnea, Obstructive/diagnosis ; Sleep Apnea, Obstructive/therapy
    Chemical Substances Oxygen (S88TT14065)
    Language English
    Publishing date 2022-10-25
    Publishing country United States
    Document type Journal Article
    ISSN 2214-8019 ; 0065-2598
    ISSN (online) 2214-8019
    ISSN 0065-2598
    DOI 10.1007/978-3-031-06413-5_13
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  9. Article: Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea.

    Gutiérrez-Tobal, Gonzalo C / Álvarez, Daniel / Vaquerizo-Villar, Fernando / Barroso-García, Verónica / Gómez-Pilar, Javier / Del Campo, Félix / Hornero, Roberto

    Advances in experimental medicine and biology

    2022  Volume 1384, Page(s) 131–146

    Abstract: The overnight polysomnography shows a range of drawbacks to diagnose obstructive sleep apnea (OSA) that have led to the search for artificial intelligence-based alternatives. Many classic machine learning methods have been already evaluated for this ... ...

    Abstract The overnight polysomnography shows a range of drawbacks to diagnose obstructive sleep apnea (OSA) that have led to the search for artificial intelligence-based alternatives. Many classic machine learning methods have been already evaluated for this purpose. In this chapter, we show the main approaches found in the scientific literature along with the most used data to develop the models, useful and large easily available databases, and suitable methods to assess performances. In addition, a range of results from selected studies are presented as examples of these methods. Very high diagnostic performances are reported in these results regardless of the approaches taken. This leads us to conclude that conventional machine learning methods are useful techniques to develop new OSA diagnosis simplification proposals and to act as benchmark for other more recent methods such as deep learning.
    MeSH term(s) Artificial Intelligence ; Humans ; Machine Learning ; Polysomnography/methods ; Sleep Apnea, Obstructive/diagnosis
    Language English
    Publishing date 2022-10-25
    Publishing country United States
    Document type Journal Article
    ISSN 2214-8019 ; 0065-2598
    ISSN (online) 2214-8019
    ISSN 0065-2598
    DOI 10.1007/978-3-031-06413-5_8
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  10. Article ; Online: A deep learning model based on the combination of convolutional and recurrent neural networks to enhance pulse oximetry ability to classify sleep stages in children with sleep apnea.

    Vaquerizo-Villar, Fernando / Alvarez, Daniel / Gutierrez-Tobal, Gonzalo C / Del Campo, Felix / Gozal, David / Kheirandish-Gozal, Leila / Penzel, Thomas / Hornero, Roberto

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

    2023  Volume 2023, Page(s) 1–4

    Abstract: Characterization of sleep stages is essential in the diagnosis of sleep-related disorders but relies on manual scoring of overnight polysomnography (PSG) recordings, which is onerous and labor-intensive. Accordingly, we aimed to develop an accurate deep- ... ...

    Abstract Characterization of sleep stages is essential in the diagnosis of sleep-related disorders but relies on manual scoring of overnight polysomnography (PSG) recordings, which is onerous and labor-intensive. Accordingly, we aimed to develop an accurate deep-learning model for sleep staging in children suffering from pediatric obstructive sleep apnea (OSA) using pulse oximetry signals. For this purpose, pulse rate (PR) and blood oxygen saturation (SpO
    MeSH term(s) Humans ; Child ; Deep Learning ; Sleep Apnea Syndromes/diagnosis ; Oximetry/methods ; Sleep Apnea, Obstructive/diagnosis ; Neural Networks, Computer ; Sleep Stages
    Language English
    Publishing date 2023-12-11
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2694-0604
    ISSN (online) 2694-0604
    DOI 10.1109/EMBC40787.2023.10341100
    Database MEDical Literature Analysis and Retrieval System OnLINE

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