Sleep scoring using polysomnography data features

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11150%2F18%3A10378647" target="_blank" >RIV/00216208:11150/18:10378647 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/18:00327424 RIV/60461373:22340/18:43915777

Výsledek na webu

<a href="https://doi.org/10.1007/s11760-018-1252-6" target="_blank" >https://doi.org/10.1007/s11760-018-1252-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s11760-018-1252-6" target="_blank" >10.1007/s11760-018-1252-6</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sleep scoring using polysomnography data features

Popis výsledku v původním jazyce

The paper is devoted to the analysis of multichannel biomedical signals acquired in the sleep laboratory. The data analyzed represent polysomnographic records of (i) 33 healthy individuals, (ii) 25 individuals with sleep apnea, and (iii) 18 individuals with sleep apnea and restless leg syndrome. The initial statistical analysis of the sleep segments points to an increase in the number of Wake stages and the decrease in REM stages with increase in age. The goal of the study is visualization of features associated with sleep stages as specified by an experienced neurologist and in their adaptive classification. The results of the support vector machine classifier are compared with those obtained by the k-nearest neighbors method, decision tree and neural network classification using sigmoidal and Bayesian transfer functions. The achieved accuracy for the classification into two classes (to separate the Wake stage from one of NonREM and REM stages) is between 85.6 and 97.5% for the given set of patients with sleep apnea. The proposed models allow adaptive modification of the model coefficients during the learning process to increase the diagnostic efficiency of sleep disorder analysis, in both the clinical and home environments.

Název v anglickém jazyce

Sleep scoring using polysomnography data features

Popis výsledku anglicky

The paper is devoted to the analysis of multichannel biomedical signals acquired in the sleep laboratory. The data analyzed represent polysomnographic records of (i) 33 healthy individuals, (ii) 25 individuals with sleep apnea, and (iii) 18 individuals with sleep apnea and restless leg syndrome. The initial statistical analysis of the sleep segments points to an increase in the number of Wake stages and the decrease in REM stages with increase in age. The goal of the study is visualization of features associated with sleep stages as specified by an experienced neurologist and in their adaptive classification. The results of the support vector machine classifier are compared with those obtained by the k-nearest neighbors method, decision tree and neural network classification using sigmoidal and Bayesian transfer functions. The achieved accuracy for the classification into two classes (to separate the Wake stage from one of NonREM and REM stages) is between 85.6 and 97.5% for the given set of patients with sleep apnea. The proposed models allow adaptive modification of the model coefficients during the learning process to increase the diagnostic efficiency of sleep disorder analysis, in both the clinical and home environments.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Signal, Image and Video Processing

ISSN

1863-1703

e-ISSN

—

Svazek periodika

12

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

9

Strana od-do

1043-1051

Kód UT WoS článku

000441392700004

EID výsledku v databázi Scopus

2-s2.0-85041913126