Recognition of Atrial Fibrilation Episodes in Heart Rate Variability Signals Using a Machine Learning Approach

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10243212" target="_blank" >RIV/61989100:27240/19:10243212 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/abstract/document/8787048" target="_blank" >https://ieeexplore.ieee.org/abstract/document/8787048</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/MIXDES.2019.8787048" target="_blank" >10.23919/MIXDES.2019.8787048</a>

Result language
angličtina
Original language name
Recognition of Atrial Fibrilation Episodes in Heart Rate Variability Signals Using a Machine Learning Approach
Original language description
Atrial fibrillation (AF) is the most common heart arrhythmia. Asymptomatic (silent) AF may be recognized during long term monitoring of the heart rate (HR) variability. The HR variability features are widely used for detection of AF. Automated classification of heart beats into AF and non-AF presented in this paper was carried out with a help of the Lagrangian Support Vector Machine. The classifier input vector included five beat-To-beat interval measures, seven adult's HR variability parameters, and four features taken from the analysis of the fetal heart rate as being characterized by high sensitivity to changes in subsequent intervals. The performance of the improved AF detection method was examined using the MIT-BIH Atrial Fibrillation Database, which includes 25 ten-hour ECG recordings. Results obtained during the classifier testing phase showed the sensitivity 95.91%, specificity 92.59%, positive predictive value 90.56%, negative predictive value 96.83%, and classification accuracy 94.00%. (C) 2019 Department of Microelectronics and Computer Science, Lodz University of Technology.
Czech name
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Czech description
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Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 26th International Conference "Mixed Design of Integrated Circuits and Systems", MIXDES 2019
ISBN
978-83-63578-15-2
ISSN
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e-ISSN
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Number of pages
6
Pages from-to
419-424
Publisher name
IEEE
Place of publication
Piscataway
Event location
Řešov
Event date
Jun 27, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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