Research and application of deep learning-based sleep staging: Data, modeling, validation, and clinical practice

H Yue, Z Chen, W Guo, L Sun, Y Dai, Y Wang… - Sleep Medicine …, 2024 - Elsevier
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 …

[HTML][HTML] IC-U-Net: a U-Net-based denoising autoencoder using mixtures of independent components for automatic EEG artifact removal

CH Chuang, KY Chang, CS Huang, TP Jung - NeuroImage, 2022 - Elsevier
Electroencephalography (EEG) signals are often contaminated with artifacts. It is imperative
to develop a practical and reliable artifact removal method to prevent the misinterpretation of …

Advanced sleep spindle identification with neural networks

L Kaulen, JTC Schwabedal, J Schneider, P Ritter… - Scientific reports, 2022 - nature.com
Sleep spindles are neurophysiological phenomena that appear to be linked to memory
formation and other functions of the central nervous system, and that can be observed in …

[HTML][HTML] Deep-spindle: An automated sleep spindle detection system for analysis of infant sleep spindles

L Wei, S Ventura, MA Ryan, S Mathieson… - Computers in Biology …, 2022 - Elsevier
Background: Sleep spindles are an indicator of the development and integrity of the central
nervous system in infants. Identifying sleep spindles manually in EEG is time-consuming …

A robust deep learning detector for sleep spindles and K-complexes: towards population norms

NI Tapia-Rivas, PA Estévez, JA Cortes-Briones - Scientific Reports, 2024 - nature.com
Sleep spindles (SSs) and K-complexes (KCs) are brain patterns involved in cognitive
functions that appear during sleep. Large-scale sleep studies would benefit from precise …

GTransU-CAP: Automatic labeling for cyclic alternating patterns in sleep EEG using gated transformer-based U-Net framework

J You, Y Ma, Y Wang - Computers in Biology and Medicine, 2022 - Elsevier
Cyclic alternating pattern (CAP) sequences are composed of cycles of alternate activation
phases (A-phases) and background phases. CAP A-phases can be further divided into three …

IoT-Assisted Automatic Driver Drowsiness Detection through Facial Movement Analysis Using Deep Learning and a U-Net-Based Architecture

S Das, S Pratihar, B Pradhan, RH Jhaveri, F Benedetto - Information, 2024 - mdpi.com
The main purpose of a detection system is to ascertain the state of an individual's eyes,
whether they are open and alert or closed, and then alert them to their level of fatigue. As a …

Visual identification of sleep spindles in EEG waveform images using deep learning object detection (YOLOv4 vs YOLOX)

M Fraiwan, N Khasawneh - Cluster Computing, 2024 - Springer
The electroencephalogram (EEG) is a tool utilized to capture the intricate electrical dynamics
within the brain, offering invaluable insights into neural activity. This method is pivotal in …

Contactless screening for sleep apnea with breathing vibration signals based on modified U-Net

Y Chen, G Ma, M Zhang, S Yang, J Yan, Z Zhang… - Sleep Medicine, 2023 - Elsevier
Background Obstructive sleep apnea (OSA) is a chronic sleep disorder characterized by
frequent cessations or reductions of breathing during sleep. Polysomnography (PSG) is a …

A personalized semi-automatic sleep spindle detection (PSASD) framework

MM Kafashan, G Gupte, P Kang, O Hyche… - Journal of Neuroscience …, 2024 - Elsevier
Background Sleep spindles are distinct electroencephalogram (EEG) patterns of brain
activity that have been posited to play a critical role in development, learning, and …