Obstructive sleep apnea is linked to severe health consequences such as hypertension,
daytime sleepiness, and cardiovascular disease. Nearly a billion people are estimated to …

Sleep-disordered breathing, ranging from habitual snoring to severe obstructive sleep
apnea, is a prevalent public health issue. Despite rising interest in sleep and awareness of …

Study Objectives: Machine learning (ML) models have been employed in the setting of sleep
disorders. This review aims to summarize the existing data about the role of ML techniques …

Objective: Obstructive sleep apnea (OSA) is diagnosed using the apnea-hypopnea index
(AHI), which is the average number of respiratory events per hour of sleep. Recently …

Objective: This paper presents a novel method to quantify cardiopulmonary dynamics for
automatic sleep apnea detection by integrating the synchrosqueezing transform (SST) …

The breathing process possesses a complex variability caused in part by the respiratory
central pattern generator in the brainstem; however, it also arises from chemical and …

Classification is one important and commonly used Machine Learning technique. The
classification effectiveness can be influenced by different factors and initial data used for the …

It is estimated that 936 million adults aged 30–69 years suffer from mild to severe obstructive
sleep apnea that can result in poor sleep quality and even endanger their lives. To facilitate …

One of the most common sleep disorders is sleep apnea. It manifests itself by episodes of
shallow breathing or pauses in breathing during the night. Diagnosis of this disease involves …

Sleep disorders and inadequate sleep are quickly becoming a substantial global health
problem. Poor sleep induces a major economical and social burden owing to direct health …