Objective. Electroencephalography (EEG) analysis has been an important tool in
neuroscience with applications in neuroscience, neural engineering (eg Brain–computer …

Complex-valued neural networks (CVNNs) have shown their excellent efficiency compared
to their real counter-parts in speech enhancement, image and signal processing …

The clinical and societal measurement of human sleep has increased exponentially in
recent years. However, unlike other fields of medical analysis that have become highly …

Clinical sleep scoring involves a tedious visual review of overnight polysomnograms by a
human expert, according to official standards. It could appear then a suitable task for modern …

Deep Learning (DL), a successful promising approach for discriminative and generative
tasks, has recently proved its high potential in 2D medical imaging analysis; however …

For automatic sleep stage classification, the existing methods mostly rely on hand-crafted
features selected from polysomnographic records. In this paper, the goal is to develop a …

Sleep is vital for one's general well-being, but it is often neglected, which has led to an
increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep …

Deep learning has achieved excellent performance in a wide range of domains, especially
in speech recognition and computer vision. Relatively less work has been done for …

Background The past few years have seen a rapid emergence of artificial intelligence (AI)-
enabled technology in the field of sleep medicine. AI refers to the capability of computer …

Several applications of complex-valued networks have been reported for computer vision
tasks like image processing and classification. However, complex-valued convolutional …