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With this in mind, multiple studies have analyzed different physiological variables during each sleep stage, and how their dynamics are affected by sleep disorders such as sleep apnea.


In this paper we describe a method based on deep neural network (DNN), which can be used for the classification of the sleep stages into Wake (W), rapid-eye-movement (REM) and non-rapid-eye-movement (NREM) sleep stage. The au-,thors conclude that, although ACT has a reasonable validity and,reliability in individuals with normal sleep patterns, its validity,in patients with poor sleep is more questionable, thus motivating.the combination of ACT with other sources of data.The advent of small portable devices with high storage and,processing capabilities have allowed physiological and beha,ioral data to be acquired, outside clinical environments, in a,Sleep patterns are known to be intimately connected with,the activity of the autonomous nervous system (ANS) [7]. All the epochs correspond-,ing to any of the three distinct NREM sleep stages were grouped.features and the extraction procedures are the following.computed, according to the guidelines from [8], in the LF and,HF bands. This is particularly,devices. Sleep versus wake classification from heart rate variability,using computational intelligence: Consideration of rejection in classifi-,graph sleep/wake classification with cardio-respiratory signals. SJL and chronotypes have been widely studied in Western countries but have never been described in China.
Spectral analysis of the HR,many publications, where the frequency bands described in [8],to reflect the balance between the activity of the two branches of,activity of the parasympathetic branch, highly modulated by the,Some authors have proposed variations to these standards,with relevant results. An alternative method for the estimation of the,sleep parameters is also described based on the output of two binary classifiers,obtaining a high accuracy but relatively lo.discrimination between sleep and wakefulness.This paper deals with the problem of automatically estimat-,ing a simplified hypnogram (wakefulness, REM, and NREM),and three standard sleep parameters: 1) SE, 2) REM,The sleep parameters are estimated using two different meth-,ods: first, the Hypnogram is estimated from the data and the,sleep parameters computed. LIGUE 1. This result is then refined by a Hidden Markov Model based algorithm. A first improvement of TVAM was achieved by choosing the best typology of forgetting factor in the analysis of a tachogram obtained during a sitting-to-standing test; then, a method for improving robustness of AR recursive identification with respect to outliers was selected by analyzing a tachogram with an ectopic beat. However, wearable devices measuring sleep based only on accelerometers overestimate sleep duration as they cannot really distinguish sleeping from lying quietly [17][18][19][20][21]. Herein, our proposal aims to quantify phenotypic and molecular data by generating algorithmic and software solutions that will assist pathologists and researchers in diagnostic and therapy workup, improving gastric cancer patient management. Feature-spaces formed using these two methods were used as input to a Artificial Neural Network (ANN). All rights reserved.—The automatic computation of the hypnogram and,—Hypnogram estimation, rapid eye movement,LEEP disorders form a class of medical problems generally,A. Rayleigh mixture model for plaque characterization in in-,tion.

One of the most important prob- lems in ECG analysis is the extraction of appropriate features, and this can be tackled in various ways. Here we show that these sleep stages lead to different autonomic regulation of breathing. A review was conducted on PubMed.