Remaining useful life (RUL) prediction is an essential component for prognostics and health
management of a system. Due to the powerful ability of nonlinear modeling, deep learning …

Emotion recognition using multi-modal physiological signals is an emerging field in affective
computing that significantly improves performance compared to unimodal approaches. The …

A novel multi-channel-based 3D convolutional neural network (3D-CNN) is proposed in this
paper to classify sleep stages. Time domain features, frequency domain features, and time …

Electroencephalography (EEG) sensors are flexible and non-invasive sensoring devices for
the measurement of electrical brain activity which is extensively used in some areas of …

Representation learning is vital for the performance of multivariate time series (MTS)-related
tasks. Given high-dimensional MTS data, researchers generally rely on deep learning …

Heterogeneous graphs, which are also called heterogeneous information networks, analyze
the different types of nodes in an information network and the different types of links between …

Sleep staging is helpful in assessing sleep quality and diagnosing sleep disorders.
However, how to adequately capture the temporal and spatial relations of the brain during …

Narcolepsy is a sleep disorder affecting millions of people worldwide and causes serious
public health problems. It is hard for doctors to correctly and objectively diagnose …

Epilepsy is a chronic brain disease, and identifying seizures based on
electroencephalogram (EEG) signals would be conducive to implement interventions to help …

Sleep staging has been widely used as an approach in sleep diagnoses at sleep clinics.
Graph neural network (GNN)-based methods have been extensively applied for automatic …