Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11150%2F18%3A10377471" target="_blank" >RIV/00216208:11150/18:10377471 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21730/18:00327451 RIV/60461373:22340/18:43915618 RIV/00179906:_____/18:10377471

Result on the web

<a href="https://doi.org/10.3390/app8050697" target="_blank" >https://doi.org/10.3390/app8050697</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/app8050697" target="_blank" >10.3390/app8050697</a>

Result language

angličtina

Original language name

Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition

Original language description

Multimodal signal analysis based on sophisticated sensors, efficient communication systems and fast parallel processing methods has a rapidly increasing range of multidisciplinary applications. The present paper is devoted to pattern recognition, machine learning, and the analysis of sleep stages in the detection of sleep disorders using polysomnography (PSG) data, including electroencephalography (EEG), breathing (Flow), and electro-oculogram (EOG) signals. The proposed method is based on the classification of selected features by a neural network system with sigmoidal and softmax transfer functions using Bayesian methods for the evaluation of the probabilities of the separate classes. The application is devoted to the analysis of the sleep stages of 184 individuals with different diagnoses, using EEG and further PSG signals. Data analysis points to an average increase of the length of the Wake stage by 2.7% per 10 years and a decrease of the length of the Rapid Eye Movement (REM) stages by 0.8% per 10 years. The mean classification accuracy for given sets of records and single EEG and multimodal features is 88.7% ( standard deviation, STD: 2.1) and 89.6% (STD:1.9), respectively. The proposed methods enable the use of adaptive learning processes for the detection and classification of health disorders based on prior specialist experience and man-machine interaction.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30103 - Neurosciences (including psychophysiology)

Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

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

Name of the periodical

Applied Sciences

ISSN

2076-3417

e-ISSN

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Volume of the periodical

8

Issue of the periodical within the volume

5

Country of publishing house

CH - SWITZERLAND

Number of pages

14

Pages from-to

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UT code for WoS article

000437326800044

EID of the result in the Scopus database

2-s2.0-85047096680