Results 151 to 160 of about 733,400 (260)

MobileNetV2: A lightweight classification model for home-based sleep apnea screening [PDF]

arXiv
This study proposes a novel lightweight neural network model leveraging features extracted from electrocardiogram (ECG) and respiratory signals for early OSA screening. ECG signals are used to generate feature spectrograms to predict sleep stages, while respiratory signals are employed to detect sleep-related breathing abnormalities.
arxiv  

Association of Obstructive Sleep Apnea Syndrome and Buerger's Disease: a Pilot Study

Acta Medica Iranica, 2015
In this study we evaluated the incidence and severity of obstructive sleep apnea and Obstructive sleep apnea syndrome in patients with thromboangiitis obliterans for reduction of crisis.
Gholam Hosein Kazemzadeh, Ali Reza Bameshki, Iman Navvabi, Seyed Hosein Ahmadi Hoseini, Mehryar Taghavi Gilani   +4 more
doaj  

What Radio Waves Tell Us about Sleep [PDF]

arXiv
The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people's bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients' homes, informing our understanding of sleep and its ...
arxiv  

Uvulopalatopharyngoplasty with tonsillectomy in the treatment of severe OSAS [PDF]

, 2009
Objective: To establish the efficacy of uvulopalatopharyngoplasty with tonsillectomy for treating selected patients with severe obstructive sleep apnea syndrome Methodology: Retrospective study of patients who underwent clinical/instrumental ...
Ballacchino, A, Dispenza, F, GALLINA, Salvatore, Kulamarva, G, SPECIALE, Riccardo   +4 more
core  

Evaluation of sleep structure in children of 2 to 12 years of age with obstructive sleep apnea proven by polysomnography

Chronic Diseases Journal
BACKGROUND: Identifying children with sleep-disordered breathing (SDB) and treating them early is essential. This study was conducted to determine the structure of sleep in children with obstructive sleep apnea (OSA).
Shabnam Jalilalghadr, Hasan Taher-Ahmadi, Hoormehr Nozari, Manouchehr Taghiloo   +3 more
doaj   +1 more source

Evaluation of human obstructive sleep apnea using computational fluid dynamics. [PDF]

, 2019
Obstructive sleep apnea (OSA) severity might be correlated to the flow characteristics of the upper airways. We aimed to investigate the severity of OSA based on 3D models constructed from CT scans coupled with computational fluid dynamics (CFD ...
Jafari, Behrouz, Lopez, Samuel, Rahai, Hamid, Shin, Jamie, Taherian, Shahab   +4 more
core  

Accurate Radar-Based Detection of Sleep Apnea Using Overlapping Time-Interval Averaging [PDF]

arXiv
Radar-based respiratory measurement is a promising tool for the noncontact detection of sleep apnea. Our team has reported that apnea events can be accurately detected using the statistical characteristics of the amplitude of respiratory displacement. However, apnea and hypopnea events are often followed by irregular breathing, reducing the detection ...
arxiv  

Continuous Sleep Depth Index Annotation with Deep Learning Yields Novel Digital Biomarkers for Sleep Health [PDF]

arXiv
Traditional sleep staging categorizes sleep and wakefulness into five coarse-grained classes, overlooking subtle variations within each stage. It provides limited information about the duration of arousal and may hinder research on sleep fragmentation and relevant sleep disorders.
arxiv  

Multimodal Sleep Stage and Sleep Apnea Classification Using Vision Transformer: A Multitask Explainable Learning Approach [PDF]

arXiv
Sleep is an essential component of human physiology, contributing significantly to overall health and quality of life. Accurate sleep staging and disorder detection are crucial for assessing sleep quality. Studies in the literature have proposed PSG-based approaches and machine-learning methods utilizing single-modality signals.
arxiv  

Multimodal Sleep Apnea Detection with Missing or Noisy Modalities [PDF]

arXiv
Polysomnography (PSG) is a type of sleep study that records multimodal physiological signals and is widely used for purposes such as sleep staging and respiratory event detection. Conventional machine learning methods assume that each sleep study is associated with a fixed set of observed modalities and that all modalities are available for each sample.
arxiv