The electroencephalogram (EEG) is considered to be a promising method for studying brain
disorders. Because of its non-invasive nature, subjects take a lower risk compared to some …

This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep
stage scoring based on raw single-channelEEG. Most of the existing methods rely on hand …

We present a novel method for automatic sleep scoring based on single-channel EEG. We
introduce the use of a deep convolutional neural network (CNN) on raw EEG samples for …

Human emotion is a physical or psychological process which is triggered either consciously
or unconsciously due to perception of any object or situation. The electroencephalogram …

Abstract In recent years, Machine Learning (ML), a key component of artificial intelligence
(AI), has become increasingly popular in data analysis and processing. ML is now widely …

Epileptic seizures are one of the most crucial neurological disorders, and their early
diagnosis will help the clinicians to provide accurate treatment for the patients. The …

Sleep stage classification is one of the most critical steps in effective diagnosis and the
treatment of sleep-related disorders. Visual inspection undertaken by sleep experts is a time …

A deep learning model, named IITNet, is proposed to learn intra-and inter-epoch temporal
contexts from raw single-channel EEG for automatic sleep scoring. To classify the sleep …

Sleep stage classification is a most important process in sleep scoring which is used to
evaluate sleep quality and diagnose sleep-related diseases. Compared to complex sleep …

Analyzing polysomnography (PSG) is an effective method for evaluating sleep health;
however, the sleep stage scoring required for PSG analysis is a time-consuming effort for an …