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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos;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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos;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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the 26th International Conference &quot;Mixed Design of Integrated Circuits and Systems&quot;, MIXDES 2019

ISBN

978-83-63578-15-2

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

419-424

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Řešov

Datum konání akce

27. 6. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—