Long Short-Term Memory for Apnea Detection based on Heart Rate Variability

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F08%3A03145665" target="_blank" >RIV/68407700:21230/08:03145665 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Long Short-Term Memory for Apnea Detection based on Heart Rate Variability
Original language description
The main drive force in apnea current diagnostic is to reduce overwhelming number of sleep disorders candidates by means of very simple-to-use, comfortable and cheap methodology. The proposed framework is based only on automatic analysis of electrocardiogram signal. The feature extraction stage was performed using methods of Heart Rate Variability and Detrended Fluctuation analysis. Feature-spaces formed using these two methods were used as input to a Long Short-Term Memory Artificial Neural Network chosen for its capability to find temporally dependencies in the data. The framework was evaluated on Challenge 2000 Physionet database yielding successful rate 82.1%, sensitivity 85.5% and specificity 80.1%.
Czech name
Detekce spánkové apnea pomocí variability srdečního rytmu
Czech description
Článek popisuje detekci spánkové apnei pomocí variability srdečního rytmu.

Publication year
2008
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 30th Annual International Conference on the IEEE Engineering in Medicine and Biology Society
ISBN
978-1-4244-1815-2
ISSN
1557-170X
e-ISSN
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Number of pages
4
Pages from-to
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Publisher name
IEEE Computer Society
Place of publication
Los Alamitos
Event location
Vancouver
Event date
Aug 20, 2008
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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