Cover & TOC
Document type
články
články
Document record
Source: BMČ - články
Title
Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine / R. Czabanski, K. Horoba, J. Wrobel, A. Matonia, R. Martinek, T. Kupka, M. Jezewski, R. Kahankova, J. Jezewski, JM. Leski,
Author
Czabanski, Robert
Department of Cybernetics, Nanotechnology and Data Processing, Silesian University of Technology, PL44100 Gliwice, Poland.

Horoba, Krzysztof
Łukasiewicz Research Network - Institute of Medical Technology and Equipment, PL 41800 Zabrze, Poland.

Wrobel, Janusz
Łukasiewicz Research Network - Institute of Medical Technology and Equipment, PL 41800 Zabrze, Poland.

Matonia, Adam
Łukasiewicz Research Network - Institute of Medical Technology and Equipment, PL 41800 Zabrze, Poland.

Martinek, Radek
Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava,708 00 Ostrava-Poruba, Czech Republic.

Kupka, Tomasz
Łukasiewicz Research Network - Institute of Medical Technology and Equipment, PL 41800 Zabrze, Poland.

Jezewski, Michal
Department of Cybernetics, Nanotechnology and Data Processing, Silesian University of Technology, PL44100 Gliwice, Poland.

Kahankova, Radana
Department of Cybernetics and Biomedical Engineering, VSB-Technical University of Ostrava,708 00 Ostrava-Poruba, Czech Republic.

Jezewski, Janusz
Łukasiewicz Research Network - Institute of Medical Technology and Equipment, PL 41800 Zabrze, Poland.

Leski, Jacek M
Department of Cybernetics, Nanotechnology and Data Processing, Silesian University of Technology, PL44100 Gliwice, Poland.

Cited source
Sensors (Basel). 2020, roč. 20, č. 3. ISSN: 1424-8220 (elektronická verze).
Date of issue
2020
Language
angličtina
Country
Švýcarsko
Document type
články
DOI
Grant number
2017/27/B/ST6/01989 (Narodowe Centrum Nauki)     vyhledat publikace
STRATEGMED2/269343/18/ NCBR/2016 (Narodowe Centrum Badań i Rozwoju)     vyhledat publikace
Pubmed ID
Link
Record number
bmc20028578
Persistent link
English Abstract
Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.
Clipboard
Further actions