Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review

Uddin, Md. Bashir; Chow, Chin Moi; Su, Steven W.

doi:10.1088/1361-6579/aaafb8

Cited by 65 publications

(53 citation statements)

References 103 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As such, the development of tools to diagnose OSA on the basis of oximetry is an active area of research. Where other recent reviews focus on diagnostic systems as a whole, this review focuses on the variables used to analyse oximetry.…”

Section: Analysis Of Oximetry To Diagnose Osamentioning

confidence: 99%

“…Many studies have investigated the accuracy of ODI as a metric for OSA diagnosis. Previous reviews have noted a great deal of variability in both specificity (≈40–100%) and sensitivity (≈30–98%), and performance varies depending on the pre‐test probability and the AHI threshold (i.e. mild: AHI ≥ 5, moderate: AHI ≥ 15 or severe disease: AHI ≥ 30) .…”

Section: Analysis Of Oximetry To Diagnose Osamentioning

confidence: 99%

“…Uddin et al . review the methods to classify OSA on the basis of oximetry and/or respiratory signals; del Campo et al . broadly review the use of oximetry in the diagnosis of OSA with a particular focus on automated classification of OSA; and Mendonca et al .…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A review of approaches for analysing obstructive sleep apnoea‐related patterns in pulse oximetry data

Terrill

2019

Respirology

View full text Add to dashboard Cite

Overnight pulse oximetry allows the relatively noninvasive estimation of peripheral blood haemoglobin oxygen saturations (SpO 2 ), and forms part of the typical polysomnogram (PSG) for investigation of obstructive sleep apnoea (OSA). While the raw SpO 2 signal can provide detailed information about OSA-related pathophysiology, this information is typically summarized with simple statistics such as the oxygen desaturation index (ODI, number of desaturations per hour). As such, this study reviews the technical methods for quantifying OSA-related patterns in oximetry data. The technical methods described in literature can be broadly grouped into four categories: (i) Describing the detailed characteristics of desaturations events; (ii) Time series statistics; (iii) Analysis of power spectral distribution (i.e. frequency domain analysis); and (d) Non-linear analysis. These are described and illustrated with examples of oximetry traces. The utilization of these techniques is then described in two applications. First, the application of detailed oximetry analysis allows the accurate automated classification of PSG-defined OSA. Second, quantifications which better characterize the severity of desaturation events are better predictors of OSA-related epidemiological outcomes than standard clinical metrics. Finally, methodological considerations and further applications and opportunities are considered.

show abstract

Section: Analysis Of Oximetry To Diagnose Osamentioning

confidence: 99%

Section: Analysis Of Oximetry To Diagnose Osamentioning

confidence: 99%

See 1 more Smart Citation

A review of approaches for analysing obstructive sleep apnoea‐related patterns in pulse oximetry data

Terrill

2019

Respirology

View full text Add to dashboard Cite

show abstract

“…Diagnostic assessment reported in the literature is characterized by a significant variability, with sensitivity ranging from 31% to 98% and specificity ranging from 41 to 100% [20].…”

Section: Visual Inspection and Conventional Indexesmentioning

confidence: 99%

“…However, limitations regarding accessibility and intrusiveness of PSG are even more significant in pediatric patients [66]. In this regard, recent 20 reports of major sleep/respiratory international societies [66,67] highlight the need for further research on screening tests for the disease, as well as the development of simplified tools able to increase accessibility to diagnostic resources. Standard PSG will be limited to complex cases were simplified techniques do not provide sufficiently high certainty or when conventional complex techniques are needed to set the treatment modality, e.g., non-invasive ventilation vs. surgery.…”

Section: Oximetry As Screening Tool For Pediatric Osasmentioning

confidence: 99%

Oximetry use in obstructive sleep apnea

Campo

Crespo

Cerezo-Hernández

et al. 2018

Expert Review of Respiratory Medicine

View full text Add to dashboard Cite

Introduction. Overnight oximetry has been proposed as an accessible, simple, and reliable technique for obstructive sleep apnea syndrome (OSAS) diagnosis. From visual inspection to advanced signal processing, several studies have demonstrated the usefulness of oximetry as a screening tool. However, there is still controversy regarding the general application of oximetry as a single screening methodology for OSAS.Areas covered. Currently, high-resolution portable devices combined with pattern recognitionbased applications are able to achieve high performance in the detection this disease. In this review, recent studies involving automated analysis of oximetry by means of advanced signal processing and machine learning algorithms are analyzed. Advantages and limitations are highlighted and novel research lines aimed at improving the screening ability of oximetry are proposed. Expert commentary. Oximetry is a cost-effective tool for OSAS screening in patients showinghigh pretest probability for the disease. Nevertheless, exhaustive analyses are still needed to further assess unattended oximetry monitoring as a single diagnostic test for sleep apnea, particularly in the pediatric population and in especial groups with significant comorbidities. In the following years, communication technologies and big data analysis will overcome current limitations of simplified sleep testing approaches, changing the detection and management of OSAS.

show abstract

Assessment of Mandibular Movement Monitoring With Machine Learning Analysis for the Diagnosis of Obstructive Sleep Apnea

Pépin

Letesson²,

Le-Dong³

et al. 2020

JAMA Netw Open

View full text Add to dashboard Cite

IMPORTANCE Given the high prevalence of obstructive sleep apnea (OSA), there is a need for simpler and automated diagnostic approaches. OBJECTIVE To evaluate whether mandibular movement (MM) monitoring during sleep coupled with an automated analysis by machine learning is appropriate for OSA diagnosis. DESIGN, SETTING, AND PARTICIPANTS Diagnostic study of adults undergoing overnight in-laboratory polysomnography (PSG) as the reference method compared with simultaneous MM monitoring at a sleep clinic in an academic institution

show abstract

Classification methods to detect sleep apnea in adults based on respiratory and oximetry signals: a systematic review

Cited by 65 publications

References 103 publications

A review of approaches for analysing obstructive sleep apnoea‐related patterns in pulse oximetry data

A review of approaches for analysing obstructive sleep apnoea‐related patterns in pulse oximetry data

Oximetry use in obstructive sleep apnea

Assessment of Mandibular Movement Monitoring With Machine Learning Analysis for the Diagnosis of Obstructive Sleep Apnea

Contact Info

Product

Resources

About