Over the past few decades, researchers have attempted to simplify and accelerate the
process of sleep stage classification through various approaches; however, only a few such …

Electroencephalography (EEG) signals classification is essential for the brain-computer
interface (BCI). Recently, energy-efficient spiking neural networks (SNNs) have shown great …

With the improvement of quality of life, people are more and more concerned about the
quality of sleep. The electroencephalogram (EEG)-based sleep stage classification is a …

Human sleep is cyclical with a period of approximately 90 minutes, implying long temporal
dependency in the sleep data. Yet, exploring this long-term dependency when developing …

Sleep staging has a very important role in diagnosing patients with sleep disorders. In
general, this task is very time-consuming for physicians to perform. Deep learning shows …

Introduction: We propose an automatic sleep stage scoring model, referred to as
SeriesSleepNet, based on convolutional neural network (CNN) and bidirectional long short …

This paper presents a lightweight deep learning (DL) model for classifying sleep stages
based on single-channel EEG. The DL model was designed to run on energy-and memory …

Sleep stage classification is crucial for sleep state monitoring and health interventions. In
accordance with the standards prescribed by the American Academy of Sleep Medicine, a …

Electroencephalography (EEG) signals are the brain signals acquired using the non-
invasive approach. Owing to the high portability and practicality, EEG signals have found …

Sleep staging is essential for assessing sleep quality and diagnosing sleep disorders.
However, sleep staging is a labor-intensive process, making it arduous to obtain large …