Anxiety and Depression in Obstructive Sleep Apnea: Prevalence and Gender/Ethnic Variance

Surani, Salim; Rao, Shweta; Surani, S.R.; Guntupalli, Bharat; Subramanian, Shyam

doi:10.2174/1573398x09666131217002138

Cited by 3 publications

(1 citation statement)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It must take at least 10 seconds to be considered sleep apnea and may occur 5 to 30 times in an hour of night sleep [1]. Some patients tolerate sleep apnea more than 300 times a night and this will consequently lead to daytime sleepiness, headaches, decreased quality of life and depression [2], [3]. Approximately, 3-7% of male subgroups and 2-5% of females in general adult categories suffer from SAS difficulties [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic sleep apnea detection using fuzzy logic

Golrou

Maghooli

Amiri

et al. 2015

2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

View full text Add to dashboard Cite

The obstructive sleep apnea (OSA) is one of the most important sleep disorders characterized by obstruction of the respiratory tract and cessation in respiratory flow level. Currently, apnea diagnosis is mainly based on the Polysomnography (PSG) testing during sleeping hours, however, recording the entire signals during nights is a very costly, timeconsuming and difficult task. The goal of this study is to provide and validate an automatic algorithm to analyze four PSGrecordings and detect the occurrence of sleep apnea by noninvasive features. Four PSG signals were extracted from oxygen saturation (SaO2), Transitional air flow (Air Flow), abdominal movements during breathing (Abdomen mov.) and movements of the chest (Thoracic mov.). We describe a fuzzy algorithm to compensate the imprecise information about the range of signal loss, regarding the expert opinions. Signal classification is implemented minute-by-minute and for 30 labeled samples of MIT/BIH data sets (acquired from PhysioNet). The obtained data from 18 apnea subjects (11 males and 7 females, mean age 43 years) were categorized in three output signals of apnea, hypopnea and normal breathing. The proposed algorithm shows proficiency in diagnosing OSA with acceptable sensitivity and specificity, respectively 86% and 87%.

show abstract

Section: Introductionmentioning

confidence: 99%

Automatic sleep apnea detection using fuzzy logic

Golrou

Maghooli

Amiri

et al. 2015

2015 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

View full text Add to dashboard Cite

show abstract

Association of Anxiety and Depression in Obstructive Sleep Apnea Patients: A Systematic Review and Meta-Analysis

Garbarino

Bardwell

Guglielmi

et al. 2018

Behavioral Sleep Medicine

133

View full text Add to dashboard Cite

A Narrative Review of Self-Reported Scales to Evaluate Depression and Anxiety Symptoms in Adult Obstructive Sleep Apnea Patients

Velescu,

Marc,

Traila

et al. 2024

Medicina

View full text Add to dashboard Cite

Background and Objectives: Obstructive sleep apnea (OSA) is a prevalent chronic condition that has been associated with mental disorders like depression and anxiety. This study intends to provide a practical overview of the most relevant self-reported and self-rating scales that assess depression and anxiety in OSA patients. Materials and Methods: A search for articles was performed using PubMed, Google Scholar, and Semantic Scholar using a combination of words for obstructive sleep apnea, depression, anxiety, and scales. The tools were ordered by type (screening and rating) and arranged chronologically according to the year of publication. Results: Three scales were identified for assessing depression, which were the Center for Epidemiologic Studies Depression Scale (CES-D), the Hospital Anxiety and Depression Scale (HADS-D), and the Patient Health Questionnaire-9 (PHQ-9). For rating depression, two scales were discussed: the Zung Self-Rating Depression Scale (SDS) and the Beck Depression Inventory (BDI), which has three versions (the BDI, the BDI-II, and the Fast Screen (BDI-FS)). For assessing anxiety, the Generalized Anxiety Disorder-7 (GAD-7) scale was identified. Two scales were reviewed for rating anxiety: the State-Trait Anxiety Inventory (STAI) and the Beck Anxiety Inventory (BAI). Each scale is accompanied by a brief description of its practicality and psychometric qualities and an analysis of its strengths and limitations. Conclusions: The findings of this review will contribute to the understanding of the importance of assessing mental health comorbidities in the context of OSA, ultimately guiding clinical practice and future research in this area.

show abstract

Anxiety and Depression in Obstructive Sleep Apnea: Prevalence and Gender/Ethnic Variance

Cited by 3 publications

References 0 publications

Automatic sleep apnea detection using fuzzy logic

Automatic sleep apnea detection using fuzzy logic

Association of Anxiety and Depression in Obstructive Sleep Apnea Patients: A Systematic Review and Meta-Analysis

A Narrative Review of Self-Reported Scales to Evaluate Depression and Anxiety Symptoms in Adult Obstructive Sleep Apnea Patients

Contact Info

Product

Resources

About