Classification and automatic scoring of arousal intensity during sleep stages using machine learning.

Arousal during sleep can result in sleep fragmentation and various physiological effects, impairing cognitive function and raising blood pressure and heart rate. However, the current definition of arousal has limitations in assessing both amplitude and duration, making it challenging to measure sleep fragmentation accurately. Moreover, there is inconsistency among inter-raters in arousal scoring, which renders it susceptible to subjective variability.

A novel sleep aid device to reduce sleep latency using air-CO mixed gas.

Introduction: Sleep is an indispensable component of human life. However, in modern times, the number of people suffering from sleep disorders, such as insomnia and sleep deprivation, has increased significantly. Therefore, to alleviate the discomfort to the patient due to lack of sleep, sleeping pills and various sleep aids are being introduced and used.

The simplification of the insomnia severity index and epworth sleepiness scale using machine learning models.

Insomnia and excessive daytime sleepiness (EDS) are the most common complaints in sleep clinics, and the cost of healthcare services associated with them have also increased significantly. Though the brief questionnaires such as the Insomnia Severity Index (ISI) and Epworth Sleepiness Scale (ESS) can be useful to assess insomnia and EDS, there are some limitations to apply for large numbers of patients. As the researches using the Internet of Things technology become more common, the need for the simplification of sleep questionnaires has been also growing.