نتایج جستجو برای: Sleep stages classification

تعداد نتایج: 773664  

Journal: :journal of electrical and computer engineering innovations 2013
r. kianzad h. montazery kordy

sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. in this paper, a combination of three kinds of classifiers are proposed which classify the eeg signal into five sleep stages including awake, n-rem (non-rapid eye movement) stage 1, n-rem stage 2, n-rem stage 3 and 4 (also called slow wave sleep), and rem. twenty-five all night recordings...

H. Montazery Kordy R. Kianzad

Sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. In this paper, a combination of three kinds of classifiers are proposed which classify the EEG signal into five sleep stages including Awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3 and 4 (also called Slow Wave Sleep), and REM. Twenty-five all night recordings...

Journal: :health, spirituality and medical ethics journal 0
mohammad reza heidari shahed university reza norouzadeh shahed university mohammad abbasi 2qom medical sciences university

background and objectives: sleep is a sign of the greatness of god. in this article sleep is described from quran and modern health sciences. methods: this is a qualitative systematic review. data were gathered from the quran, related islamic narratives and literatures. words that were searched included sleep,sleep stages, subaat, hojoo, ruqood, nu’ass, sinah. results: the results showed that t...

Journal: :CoRR 2016
Endang Purnama Giri Mohamad Ivan Fanany Aniati Murni Arymurthy

Sleep stages pattern provides important clues in diagnosing the presence of sleep disorder. By analyzing sleep stages pattern and extracting its features from EEG, EOG, and EMG signals, we can classify sleep stages. This study presents a novel classification model for predicting sleep stages with a high accuracy. The main idea is to combine the generative capability of Deep Belief Network (DBN)...

Journal: :Indian Journal of Science and Technology 2015

2015
Xi Yang Xi Long Reinder Haakma Ronald M. Aarts X. Yang X. Long R. Haakma R. M. Aarts

This report comprises two goals. The first goal is to investigate to what extent cardiorespiratory activity can provide information about sleep stages; the second goal is the analysis of overnight sleep with respect to finer micro-stages compared with the current definition of sleep stages. To extract information regarding sleep stages from respiratory effort and cardiac activity, we use autore...

2014
K. Venkatesh K. Mohanavelu K. Adalarasu

The usual method for sleep stages classification is visual inspection method by sleep specialist. It uses eight EEG channels (O1, O2, T3, T4, C3, C4, Fp1 and Fp2), EOG and also EMG for sleep analysis. This method consumes more time (hours) for sleep stages classification. Some brain disorders like narcolepsy (excessive day time sleepiness) requires real-time monitoring of sleep states which is ...

Journal: :Entropy 2014
José Luis Rodríguez-Sotelo Alejandro Osorio-Forero Alejandro Jiménez-Rodríguez David Cuesta-Frau Eva M. Cirugeda-Roldán Diego Hernán Peluffo-Ordóñez

Sleep is a growing area of research interest in medicine and neuroscience. Actually, one major concern is to find a correlation between several physiologic variables and sleep stages. There is a scientific agreement on the characteristics of the five stages of human sleep, based on EEG analysis. Nevertheless, manual stage classification is still the most widely used approach. This work proposes...

Journal: :Computer methods and programs in biomedicine 2013
Varun Bajaj Ram Bilas Pachori

In this paper, a new method for automatic sleep stage classification based on time-frequency image (TFI) of electroencephalogram (EEG) signals is proposed. Automatic classification of sleep stages is an important part for diagnosis and treatment of sleep disorders. The smoothed pseudo Wigner-Ville distribution (SPWVD) based time-frequency representation (TFR) of EEG signal has been used to obta...

2011
GIRISHA GARG VIJANDER SINGH SUSHIL CHANDRA

This paper addresses the automated scoring of sleep stages using Electroencephalograph (EEG). The change in the Sleep Stages is accompanied by changes in the frequency spectrum of the EEG signals. A novel method based on Relative Wavelet Energy based Neuro-fuzzy is proposed to perform automatic sleep stages classification. Features extracted from 30-second epoch of (EEG) using relative wavelet ...

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