Detail publikace

Cardiac Pathologies Detection and Classification in 12-lead ECG

SMÍŠEK, R. NĚMCOVÁ, A. MARŠÁNOVÁ, L. SMITAL, L. VÍTEK, M. KOZUMPLÍK, J.

Originální název

Cardiac Pathologies Detection and Classification in 12-lead ECG

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

Background: Automatic detection and classification of cardiac abnormalities in ECG is one of the basic and often solved problems. The aim of this paper is to present a proposed algorithm for ECG classification into 19 classes. This algorithm was created within PhysioNet/CinC Challenge 2020, name of our team was HITTING. Methods: Our algorithm detects each pathology separately according to the extracted features and created rules. Signals from the 6 databases were used. Detector of QRS complexes, T-waves and P-waves including detection of their boundaries was designed. Then, the most common morphology of the QRS was found in each record. All these QRS were averaged. Features were extracted from the averaged QRS and from intervals between detected points. Appropriate features and rules were set using classification trees. Results: Our approach achieved a challenge validation score of 0.435, and full test score of 0.354, placing us 11 out of 41 in the official ranking. Conclusion: The advantage of our algorithm is easy interpretation. It is obvious according to which features algorithm decided and what thresholds were set.

Klíčová slova

ECG classification, cardiac pathologies classification

Autoři

SMÍŠEK, R.; NĚMCOVÁ, A.; MARŠÁNOVÁ, L.; SMITAL, L.; VÍTEK, M.; KOZUMPLÍK, J.

Vydáno

30. 12. 2020

Nakladatel

IEEE

Místo

Rimini, Italy

ISSN

2325-887X

Periodikum

Computing in Cardiology

Ročník

47

Číslo

1

Stát

Spojené státy americké

Strany od

1

Strany do

4

Strany počet

4

URL

Plný text v Digitální knihovně

BibTex

@inproceedings{BUT166076,
  author="Radovan {Smíšek} and Andrea {Němcová} and Lucie {Šaclová} and Lukáš {Smital} and Martin {Vítek} and Jiří {Kozumplík}",
  title="Cardiac Pathologies Detection and Classification in 12-lead ECG",
  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.2020.171",
  issn="2325-887X",
  url="http://www.cinc.org/archives/2020/pdf/CinC2020-171.pdf"
}