Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting

AR Hassan, MIH Bhuiyan - Computer methods and programs in …, 2017 - Elsevier
Background and objective: Automatic sleep staging is essential for alleviating the burden of
the physicians of analyzing a large volume of data by visual inspection. It is also a …

Robust sleep stage classification with single-channel EEG signals using multimodal decomposition and HMM-based refinement

D Jiang, Y Lu, MA Yu, W Yuanyuan - Expert Systems with Applications, 2019 - Elsevier
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 …

Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks

L Zhang, D Fabbri, R Upender, D Kent - Sleep, 2019 - academic.oup.com
Abstract Study Objectives Polysomnography (PSG) scoring is labor intensive and suffers
from variability in inter-and intra-rater reliability. Automated PSG scoring has the potential to …

Expert-level automated sleep staging of long-term scalp electroencephalography recordings using deep learning

M Abou Jaoude, H Sun, KR Pellerin, M Pavlova… - Sleep, 2020 - academic.oup.com
Abstract Study Objectives Develop a high-performing, automated sleep scoring algorithm
that can be applied to long-term scalp electroencephalography (EEG) recordings. Methods …

Automatic sleep stage classification with single channel EEG signal based on two-layer stacked ensemble model

J Zhou, G Wang, J Liu, D Wu, W Xu, Z Wang, J Ye… - IEEE …, 2020 - ieeexplore.ieee.org
Sleep stage classification, including wakefulness (W), rapid eye movement (REM), and non-
rapid eye movement (NREM) which includes three sleep stages that describe the depth of …

A two-step automatic sleep stage classification method with dubious range detection

T Sousa, A Cruz, S Khalighi, G Pires… - Computers in biology and …, 2015 - Elsevier
Background The limitations of the current systems of automatic sleep stage classification
(ASSC) are essentially related to the similarities between epochs from different sleep stages …

Automatic stage scoring of single-channel sleep EEG using CEEMD of genetic algorithm and neural network

S Sheykhivand, T Yousefi Rezaii, Z Mousavi… - … Intelligence in Electrical …, 2018 - isee.ui.ac.ir
Using an intelligent method to automatically detect sleep patterns in medical applications is
one of the most important challenges in recent years to reduce the workload of physicians in …

Reconstruction of physiological signals using iterative retraining and accumulated averaging of neural network models

J McBride, A Sullivan, H **a, A Petrie… - Physiological …, 2011 - iopscience.iop.org
Real-time monitoring of vital physiological signals is of significant clinical relevance.
Disruptions in the signals are frequently encountered and make it difficult for precise …

Knowledge-based decision system for automatic sleep staging using symbolic fusion in a turing machine-like decision process formalizing the sleep medicine …

A Ugon, A Kotti, B Séroussi, K Sedki, J Bouaud… - Expert Systems with …, 2018 - Elsevier
Automatic sleep staging is challenging since several issues need to be addressed.
Traditional approaches from literature do not satisfy medical experts since they do not reflect …

[PDF][PDF] Using Sleep Monitoring System for Estimating and Analysing the Sleep Stages

Y Luo, P Wang, K Yu, S Luo… - Pakistan Journal of …, 2021 - researcherslinks.com
Online First Article Page 1 Online First Article Using Sleep Monitoring System for Estimating and
Analysing the Sleep Stages Yuzhou Luo1, ** Wang1, Kaijun Yu2*, Song Luo3 and Longjie …