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MARŠÁNOVÁ, L. NĚMCOVÁ, A. SMÍŠEK, R. SMITAL, L. VÍTEK, M.
Original Title
Single-Feature Method for Fast Atrial Fibrillation Detection in ECG Signals
Type
conference paper
Language
English
Original Abstract
Atrial fibrillation (AF) is the most common arrhthmia in adults and is associated with higher risk of heart failure or death. Here, we introduce simple and efficient method for automatic AF detection based on symbolic dynamics and Shannon entropy. This method comprises of three parts. Firstly, QRS complex detection is provided, than the raw RR sequence is transformed into a sequence of specific symbols and subsequently into a word sequence and finally, Shannon entropy of the word sequence is calculated. According to the value of Shannon entropy, it is decided, whether AF is present in the current cardiac beat. We achieved sensitivity Se=96.32% and specificity Sp=98.61 on MIT-BIH Atrial Fibrillation database, Se=91.30% and Sp=90.80% on MIT-BIH Arrhythmia database, Se=95.6% and Sp=80.27% for CinC Challenge database 2020. The achieved results of our one-feature method are comparable with other authors of more complicated and computationally expensive methods.
Keywords
ECG, atrial fibrillation, phasor transform, symbolic dynamic
Authors
MARŠÁNOVÁ, L.; NĚMCOVÁ, A.; SMÍŠEK, R.; SMITAL, L.; VÍTEK, M.
Released
28. 12. 2020
Publisher
IEEE
Location
Rimini, Italy
ISBN
2325-887X
Periodical
Computing in Cardiology
Year of study
47
Number
1
State
United States of America
Pages from
Pages to
4
Pages count
URL
http://www.cinc.org/archives/2020/pdf/CinC2020-335.pdf
Full text in the Digital Library
http://hdl.handle.net/11012/196703
BibTex
@inproceedings{BUT166074, author="Lucie {Šaclová} and Andrea {Němcová} and Radovan {Smíšek} and Lukáš {Smital} and Martin {Vítek}", title="Single-Feature Method for Fast Atrial Fibrillation Detection in ECG Signals", booktitle="Computing in Cardiology 2020", year="2020", journal="Computing in Cardiology", volume="47", number="1", pages="1--4", publisher="IEEE", address="Rimini, Italy", doi="10.22489/CinC.2020.335", issn="2325-887X", url="http://www.cinc.org/archives/2020/pdf/CinC2020-335.pdf" }