Automated sleep disorder detection is challenging because physiological symptoms can
vary widely. These variations make it difficult to create effective sleep disorder detection …

With the increase in biosensors and data collection devices in the healthcare industry,
artificial intelligence and machine learning have attracted much attention in recent years. In …

The gold standard approach to diagnose obstructive sleep apnea (OSA) in children is
overnight in-lab polysomnography (PSG), which is labor-intensive for clinicians and onerous …

Sleep apnea is a sleep disorder that can cause serious health problems. An Artificial Neural
Network classifier to detect sleep apnea has been presented in this paper by utilizing the …

Deep-learning algorithms have been proposed to analyze overnight airflow (AF) and
oximetry (SpO 2) signals to simplify the diagnosis of pediatric obstructive sleep apnea …

An increasing number of patients and a lack of awareness about obstructive sleep apnea is
a point of concern for the healthcare industry. Polysomnography is recommended by health …

Sleep apnea in children is a major health problem affecting one to five percent of children (in
the US). If not treated in a timely manner, it can also lead to other physical and mental health …

Accurate detection of obstructive sleep apnea (OSA) with a single-lead electrocardiogram
(ECG) signal is highly desirable for the timely treating of OSA patients. However, due to the …

Obstructive sleep apnea (OSA) is a common sleep disease which may cause many serious
health problems, therefore timely diagnosis and treatment could bring important help for …

Sleep apnea (SLA) is a respiratory-related sleep disorder that affects a major proportion of
the population. The gold standard in sleep testing, polysomnography, is costly, inconvenient …