Sleep is a key marker of health, as it can either be a cause or a consequence. It is
traditionally studied in clinical environments using dedicated medical devices. Recent …

Building machine learning models using EEG recorded outside of the laboratory setting
requires methods robust to noisy data and randomly missing channels. This need is …

The streaming and recording of smartphone sensor signals is desirable for mHealth,
telemedicine, environmental monitoring and other applications. Time series data gathered in …

Sleep plays a significant role in our health and well-being. We can see an increasing
amount of work in HCI exploring the design for better sleep. However, most of these works …

For gauging the drowsiness, various algorithms have been proposed, but the maximum of
approaches tries to gauge the facial expression and even change in the skin. In this paper …

The study provides a description of electroencephalography (EEG) advancements and their
application in diagnosing and assessing various neurological diseases over the previous …

This study presents automated sleep staging on a large number of sleep
electroencephalography (EEG) recordings collected using the Muse S headband. Two …

One of the main problems in the diagnosis of sleep disorders is the act of annotating the
sleep stages on the recording results of each patient. The annotation of sleep stages is …

Recent technological advances have opened many new possibilities for health appli-
cations. Next generation of networks allows real-time monitoring, collaboration, and …

Sleep behavior is traditionally monitored with polysomnography, and sleep stage patterns
are a key marker for sleep quality used to detect anomalies and diagnose diseases. With the …