Results 61 to 70 of about 25,096 (221)
Recent Advances in Computer Audition for Diagnosing COVID-19: An Overview [PDF]
arXiv, 2020 Computer audition (CA) has been demonstrated to be efficient in healthcare domains for speech-affecting disorders (e.g., autism spectrum, depression, or Parkinson's disease) and body sound-affecting abnormalities (e. g., abnormal bowel sounds, heart murmurs, or snore sounds).
arxiv
Snoring in a cohort of obese children: association with palate position and nocturnal desaturations
Journal of Pediatric and Neonatal Individualized Medicine, 2016 Purpose of the study: Frequency of habitual snoring is significantly higher in obese than in normal-weight subjects. Obesity and adeno-tonsillar size are risk factors of snoring.
Marco Zaffanello +7 more
doaj +1 more source
Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data [PDF]
arXiv, 2019 One of the frontier issues that severely hamper the development of automatic snore sound classification (ASSC) associates to the lack of sufficient supervised training data. To cope with this problem, we propose a novel data augmentation approach based on semi-supervised conditional Generative Adversarial Networks (scGANs), which aims to automatically ...
arxiv
Habitual Snoring in school-aged children: environmental and biological predictors
Respiratory Research, 2010 Background Habitual snoring, a prominent symptom of sleep-disordered breathing, is an important indicator for a number of health problems in children.
Wu Shenghu +5 more
doaj +1 more source
Electrocardiographic signs of pulmonary hypertension in children who snore. [PDF]
, 1981 A R Wilkinson +3 more
openalex +1 more source
SimuSOE: A Simulated Snoring Dataset for Obstructive Sleep Apnea-Hypopnea Syndrome Evaluation during Wakefulness [PDF]
arXivObstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a prevalent chronic breathing disorder caused by upper airway obstruction. Previous studies advanced OSAHS evaluation through machine learning-based systems trained on sleep snoring or speech signal datasets.
arxiv
Deep Learning Features for Robust Detection of Acoustic Events in Sleep-Disordered Breathing [PDF]
arXiv, 2019 Sleep-disordered breathing (SDB) is a serious and prevalent condition, and acoustic analysis via consumer devices (e.g. smartphones) offers a low-cost solution to screening for it. We present a novel approach for the acoustic identification of SDB sounds, such as snoring, using bottleneck features learned from a corpus of whole-night sound recordings ...
arxiv
Евразийский Кардиологический Журнал, 2016 The aim. The study and analysis of cardiac arrhythmias, heart rhythm variability in patients with ischemic heart disease, obstructive sleep apnea and primary snoring. Material and methods.
E. S. Tarasik +2 more
doaj +1 more source