Comparative Study of Deep Learning Based Sleep Scoring Methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F20%3A43921068" target="_blank" >RIV/60461373:22340/20:43921068 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9296996" target="_blank" >https://ieeexplore.ieee.org/document/9296996</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ELECOM49001.2020.9296996" target="_blank" >10.1109/ELECOM49001.2020.9296996</a>
Alternative languages
Result language
angličtina
Original language name
Comparative Study of Deep Learning Based Sleep Scoring Methods
Original language description
The paper is devoted to the analysis of multichannel biomedical signals acquired in the sleep laboratory. The whole data set includes 29 overnight records of electroencephalogram (EEG), electrooculogram (EOG), electrocardiogram (ECG), sound and movement data observed with a sampling frequency of 200 Hz, and breathing records (Flow) acquired with a sampling frequency of 10 Hz, among others. The methodology used for their processing includes their filtering, feature extraction and classification using both standard and deep learning classification methods as well. The goal of the paper is (i) in presentation of the deep learning machine learning in the frequency domain without any specification of features, (ii) in comparison with results obtained by the classical approach based on the two layer neural network model and initial specification of signal features, and (iii) in comparison of separation of the Wake sleep stage from the REM, NonRem1, NonREM2, and NonREM3 sleep stages using the deep learning method. The best separation accuracy of 92.12 % (with the loss value 0.19) was achieved for the separation of the Wake and NonREM3 stages for a single EEG channel and data segments 30s long. Results suggest that the deep learning strategy can help with sleep stages classification in the clinical environment.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Emerging Trends in Electrical, Electronic and Communications Engineering (ELECOM 2020)
ISBN
978-1-72815-708-5
ISSN
1876-1100
e-ISSN
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Number of pages
6
Pages from-to
28-33
Publisher name
Springer International Publishing Switzerland
Place of publication
Cham
Event location
Balaclava
Event date
Nov 25, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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