Multimodal Classification of Obstructive Sleep Apnea Using Feature Level Fusion

2017 ◽  
Author(s):  
Gokhan Memis ◽  
Mustafa Sert
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Multimodal Classification ◽  
Obstructive Sleep ◽  
Feature Level Fusion ◽  
Level Fusion

scholarly journals Tormented Sunrises

2019 ◽  
pp. 418-434
Author(s):  
Maha Alattar
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
General Population ◽  
Sleep Disorders ◽  
Headache Disorders ◽  
Distinct Subset ◽  
Obstructive Sleep ◽  
High Prevalence ◽  
The Relationship

This chapter covers the relationship between sleep-related headaches and sleep disorders such as obstructive sleep apnea (OSA). Sleep apnea headache (SAH), a type of sleep-related headache that is classified in the International Classification of Headache Disorders, is a distinct subset of headache that is caused by OSA and occurs distinctly on awakening. Once recognized, treatment of OSA is associated with significant improvement in, and often resolution of, SAH. Given the high prevalence of headaches in the general population, sleep disorders must be considered in the evaluation of patients with headaches. A comprehensive sleep evaluation should be an integral part of the assessment of headache disorders. Sleep apnea headache and other types of headaches associated with sleep are reviewed in this chapter.

The diagnostic method has a strong influence on classification of obstructive sleep apnea

2015 ◽  
Vol 24 (6) ◽  
pp. 730-738 ◽  
Author(s):  
Pierre Escourrou ◽  
Ludger Grote ◽  
Thomas Penzel ◽  
Walter T. Mcnicholas ◽  
Johan Verbraecken ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Strong Influence ◽  
Diagnostic Method ◽  
Obstructive Sleep

Evolution-based configuration optimization of a Deep Neural Network for the classification of Obstructive Sleep Apnea episodes

2019 ◽  
Vol 98 ◽  
pp. 377-391 ◽  
Author(s):  
Ivanoe De Falco ◽  
Giuseppe De Pietro ◽  
Antonio Della Cioppa ◽  
Giovanna Sannino ◽  
Umberto Scafuri ◽  
...  
Keyword(s):  
Neural Network ◽  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Deep Neural Network ◽  
Configuration Optimization ◽  
Obstructive Sleep

Identifying Obstructive Sleep Apnea in Administrative Data

2015 ◽  
Vol 123 (2) ◽  
pp. 253-263 ◽  
Author(s):  
Daniel I. McIsaac ◽  
Andrea Gershon ◽  
Duminda Wijeysundera ◽  
Gregory L. Bryson ◽  
Neal Badner ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Airway Pressure ◽  
Positive Airway Pressure ◽  
International Classification Of Diseases ◽  
International Classification ◽  
Diagnostic Codes ◽  
Obstructive Sleep ◽  
Classification Of Diseases

Abstract Background: Health administrative (HA) databases are increasingly used to identify surgical patients with obstructive sleep apnea (OSA) for research purposes, primarily using diagnostic codes. Such means to identify patients with OSA are not validated. The authors determined the accuracy of case-ascertainment algorithms for identifying patients with OSA with the use of HA data. Methods: Clinical data derived from an academic health sciences network within a universal health insurance plan were used as the reference standard. The authors linked patients to HA data and retrieved all claims in the 2 yr before surgery to determine the presence of any diagnostic codes, diagnostic procedures, or therapeutic interventions consistent with OSA. Results: The authors identified 4,965 patients (2003 to 2012) who underwent preoperative polysomnogram. Of these, 4,353 patients were linked to HA data; 2,427 of these (56%) had OSA based on diagnosis by a sleep physician or the apnea hypopnea index. A claim for a polysomnogram and receipt of a positive airway pressure device had a sensitivity, specificity, and positive likelihood ratio (+LR) for OSA of 19, 98, and 10.9%, respectively. An International Classification of Diseases, Tenth Revision, code for sleep apnea in hospitalization abstracts was 9% sensitive and 98% specific (+LR, 4.5). A physician billing claim for OSA (International Classification of Diseases, Ninth Revision, 780.5) was 58% sensitive and 38% specific (+LR, 0.9). A polysomnogram and a positive airway pressure device or any code for OSA was 70% sensitive and 36% specific (+LR, 1.1). Conclusions: No code or combination of codes provided a +LR high enough to adequately identify patients with OSA. Existing studies using administrative codes to identify OSA should be interpreted with caution.

scholarly journals Endoscopic classification of pharyngeal stenosis pattern in obstructive sleep apnea hypopnea syndrome

2002 ◽  
Vol 56 (3) ◽  
pp. 317-318 ◽  
Author(s):  
SHIGERU HIGAMI ◽  
YUICHI INOUE ◽  
YUMIKO HIGAMI ◽  
HIROMI TAKEUCHI ◽  
HISAAKI IKOMA
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Obstructive Sleep ◽  
Endoscopic Classification ◽  
Hypopnea Syndrome
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