Pilot Study of Sleep Apnea Detection with Wavelet Transform
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F17%3A43914777" target="_blank" >RIV/60461373:22340/17:43914777 - isvavai.cz</a>
Result on the web
<a href="http://www2.humusoft.cz/www/papers/tcp2017/032_schatz.pdf" target="_blank" >http://www2.humusoft.cz/www/papers/tcp2017/032_schatz.pdf</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Pilot Study of Sleep Apnea Detection with Wavelet Transform
Original language description
The sleep apnea syndrom is defined as repeated pauses in breathing during sleep period which leads to interrupts in sleep and decreases in oxyhemoglobin saturation. It is well understood that quantity and quality of sleep could significantly affect work productivity. In this study multimodal analysis of breathing is done with two different sensors. The first sensor measures nasal air flow and the second sensor measure abdomen effort during breathing. As it is needed to manually go through records of whole night sleep to confirm some of an automatic classification of events that can disturb sleep, it is very important to have accurate classifier. This papers aim is to present results of pilot study of competitive neural network (CNN) classifier based on Wavelet transform, with which is possible to evaluate sleep apnea from multimodal breathing data with accuracy of 94.2 % with comparison to classification of Sleep apnea by doctor. Evaluation of the whole output of CNN is complicated as the neural network was trained without target data. It can detect all apnea events from length of 5 seconds, including those that are missing in the classification by a doctor.
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
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2017
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
24th Annual Conference Proceedings Technical Computing Prague 2017
ISBN
978-80-7592-002-7
ISSN
2336-1662
e-ISSN
neuvedeno
Number of pages
11
Pages from-to
"32-1"-"32-11"
Publisher name
Vysoká škola chemicko-technologická v Praze
Place of publication
Praha
Event location
Praha
Event date
Nov 8, 2017
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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