Background Electrocardiogram (ECG) is one of the most common noninvasive diagnostic
tools that can provide useful information regarding a patient's health status. Deep learning …

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 …

In the past decade, deep learning models have been applied to bio-sensors used in a body
sensor network for prediction. Given recent innovations in this field, the prediction accuracy …

This study proposed two EEG analysis methods for diagnosis and monitoring of Parkinson's
disease. By combining time–frequency analysis with deep learning, tunable Q-factor wavelet …

EEG Signal Processing and Machine Learning Explore cutting edge techniques at the
forefront of electroencephalogram research and artificial intelligence from leading voices in …

To quantify the irregularity of data, there are a number of entropy measures each with its
own advantages and disadvantages. In this pilot study, a new concept, namely ensemble …

Nonlinear techniques have found an increasing interest in the dynamical analysis of various
kinds of systems. Among these techniques, entropy-based metrics have emerged as …

Sleep is one of the most important human physiological activities, and plays an essential
role in human health. Polysomnography (PSG) is the gold standard for measuring sleep …

Introduction Sleep is an essential function to sustain a healthy life, and sleep dysfunction
can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) …

With the emergence of new sensor technologies, such as fiber optic sensors (FOSs),
compared to traditional mechanical sensors, unobtrusive sleep monitoring has been a …