Comparison of Linear and Non-linear Dimension Reduction Techniques for Automatic Apnea Detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023752%3A_____%2F19%3A43920217" target="_blank" >RIV/00023752:_____/19:43920217 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21460/19:00337045

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/8969972" target="_blank" >https://ieeexplore.ieee.org/document/8969972</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/EHB47216.2019.8969972" target="_blank" >10.1109/EHB47216.2019.8969972</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Comparison of Linear and Non-linear Dimension Reduction Techniques for Automatic Apnea Detection

Popis výsledku v původním jazyce

Oronasal thermal sensor signal recording within sleeping period was analyzed in sense of detection of respiratory events. The goal was to evaluate what extent an up to date nonlinear dimension reduction technique increases performance of apnea classification compared to classifications based on linear dimension reduction and raw data. Based on an extensive database of recordings in apneic patients, we concluded that a non-linear approach didn&apos;t lead to better classification performance in comparison with a linear decomposition technique. We suggested, that it was due to the signal amplitude is naturally the main feature of respiratory events. However, nonlinear methods do not naturally maintain the amplitude based structure in data. Due to this fact we get the worse signal representation in comparison with linear methods.

Název v anglickém jazyce

Comparison of Linear and Non-linear Dimension Reduction Techniques for Automatic Apnea Detection

Popis výsledku anglicky

Oronasal thermal sensor signal recording within sleeping period was analyzed in sense of detection of respiratory events. The goal was to evaluate what extent an up to date nonlinear dimension reduction technique increases performance of apnea classification compared to classifications based on linear dimension reduction and raw data. Based on an extensive database of recordings in apneic patients, we concluded that a non-linear approach didn&apos;t lead to better classification performance in comparison with a linear decomposition technique. We suggested, that it was due to the signal amplitude is naturally the main feature of respiratory events. However, nonlinear methods do not naturally maintain the amplitude based structure in data. Due to this fact we get the worse signal representation in comparison with linear methods.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

2019 7th E-Health and Bioengineering Conference, EHB 2019

ISBN

978-1-72812-603-6

ISSN

—

e-ISSN

2575-5145

Počet stran výsledku

4

Strana od-do

"Article number 8969972"

Název nakladatele

IEEE Xplore

Místo vydání

online

Místo konání akce

Lasi, Romania

Datum konání akce

21. 11. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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