Background and objective: Single-channel EEG is the most popular choice of sensing
modality in sleep staging studies, because it widely conforms to the sleep staging …

Objective. Computerized classification of sleep stages based on single-lead
electroencephalography (EEG) signals is important, but still challenging. In this paper, we …

The temporal context information between sleep stage sequence contains sleep transition
rules, which is important for improving sleep staging performance. Existing multi-task …

Purpose: Sleep is a subconscious state, and the brain is active during it. Automatic
classification of sleep stages can help identify various diseases. In recent years, automatic …

Sleep staging is an important method to diagnose and treat insomnia, sleep apnea, and
other sleep disorders. Compared with the multi-channel automatic sleep staging system, the …

Deep neural networks can provide accurate automated classification of human sleep signals
into sleep stages that enables more effective diagnosis and treatment of sleep disorders. We …

Objective. Sleep staging studies on single-channel EEG mainly exploit deep learning
methods that combine convolutional neural networks (CNNs) and recurrent neural networks …

This study aimed to assess liver fibrosis in rabbits by deep learning models based on
acoustic nonlinearity maps. Injection of carbon tetrachloride was used to induce liver …