ECG features and methods for automatic classification of
ventricular premature and ischemic heartbeats: A comprehensive
experimental study

J 2017

ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study

MARŠÁNOVÁ, Lucie, Marina RONZHINA, Radovan SMÍŠEK, Martin VÍTEK, Andrea NĚMCOVÁ et. al.

Základní údaje

Originální název

ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study

Autoři

MARŠÁNOVÁ, Lucie (203 Česká republika), Marina RONZHINA (203 Česká republika), Radovan SMÍŠEK (203 Česká republika), Martin VÍTEK (203 Česká republika), Andrea NĚMCOVÁ (203 Česká republika), Lukáš SMITAL (203 Česká republika) a Marie NOVÁKOVÁ (203 Česká republika, garant, domácí)

Vydání

Scientific Reports, LONDON, NATURE PUBLISHING GROUP, 2017, 2045-2322

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30105 Physiology

Stát vydavatele

Velká Británie a Severní Irsko

Utajení

není předmětem státního či obchodního tajemství

Impakt faktor

Impact factor: 4.122

Kód RIV

RIV/00216224:14110/17:00097888

Organizační jednotka

Lékařská fakulta

DOI

http://dx.doi.org/10.1038/s41598-017-10942-6

UT WoS

000410064000075

Klíčová slova anglicky

ECG features

Štítky

EL OK

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 20. 3. 2018 17:43, Soňa Böhmová

Anotace

V originále

Accurate detection of cardiac pathological events is an important part of electrocardiogram (ECG) evaluation and subsequent correct treatment of the patient. The paper introduces the results of a complex study, where various aspects of automatic classification of various heartbeat types have been addressed. Particularly, non-ischemic, ischemic (of two different grades) and subsequent ventricular premature beats were classified in this combination for the first time. ECGs recorded in rabbit isolated hearts under non-ischemic and ischemic conditions were used for analysis. Various morphological and spectral features (both commonly used and newly proposed) as well as classification models were tested on the same data set. It was found that: a) morphological features are generally more suitable than spectral ones; b) successful results (accuracy up to 98.3% and 96.2% for morphological and spectral features, respectively) can be achieved using features calculated without time-consuming delineation of QRS-T segment; c) use of reduced number of features (3 to 14 features) for model training allows achieving similar or even better performance as compared to the whole feature sets (10 to 29 features); d) k-nearest neighbours and support vector machine seem to be the most appropriate models (accuracy up to 98.6% and 93.5%, respectively).

Návaznosti

MUNI/A/1355/2016, interní kód MU

Název: Kardiovaskulární systém očima molekulární fyziologie

Investor: Masarykova univerzita, Kardiovaskulární systém očima molekulární fyziologie, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

MARŠÁNOVÁ, Lucie, Marina RONZHINA, Radovan SMÍŠEK, Martin VÍTEK, Andrea NĚMCOVÁ, Lukáš SMITAL a Marie NOVÁKOVÁ. ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study. Scientific Reports. LONDON: NATURE PUBLISHING GROUP, 2017, roč. 7, č. 11239, s. 1-11. ISSN 2045-2322. Dostupné z: https://dx.doi.org/10.1038/s41598-017-10942-6.

@article{1392275,
   author = {Maršánová, Lucie and Ronzhina, Marina and Smíšek, Radovan and Vítek, Martin and Němcová, Andrea and Smital, Lukáš and Nováková, Marie},
   article_location = {LONDON},
   article_number = {11239},
   doi = {http://dx.doi.org/10.1038/s41598-017-10942-6},
   keywords = {ECG features},
   language = {eng},
   issn = {2045-2322},
   journal = {Scientific Reports},
   title = {ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study},
   volume = {7},
   year = {2017}
}

TY  - JOUR
ID  - 1392275
AU  - Maršánová, Lucie - Ronzhina, Marina - Smíšek, Radovan - Vítek, Martin - Němcová, Andrea - Smital, Lukáš - Nováková, Marie
PY  - 2017
TI  - ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study
JF  - Scientific Reports
VL  - 7
IS  - 11239
SP  - 1-11
EP  - 1-11
PB  - NATURE PUBLISHING GROUP
SN  - 20452322
KW  - ECG features
N2  - Accurate detection of cardiac pathological events is an important part of electrocardiogram (ECG) evaluation and subsequent correct treatment of the patient. The paper introduces the results of a complex study, where various aspects of automatic classification of various heartbeat types have been addressed. Particularly, non-ischemic, ischemic (of two different grades) and subsequent ventricular premature beats were classified in this combination for the first time. ECGs recorded in rabbit isolated hearts under non-ischemic and ischemic conditions were used for analysis. Various morphological and spectral features (both commonly used and newly proposed) as well as classification models were tested on the same data set. It was found that: a) morphological features are generally more suitable than spectral ones; b) successful results (accuracy up to 98.3% and 96.2% for morphological and spectral features, respectively) can be achieved using features calculated without time-consuming delineation of QRS-T segment; c) use of reduced number of features (3 to 14 features) for model training allows achieving similar or even better performance as compared to the whole feature sets (10 to 29 features); d) k-nearest neighbours and support vector machine seem to be the most appropriate models (accuracy up to 98.6% and 93.5%, respectively).
ER  -

MARŠÁNOVÁ, Lucie, Marina RONZHINA, Radovan SMÍŠEK, Martin VÍTEK, Andrea NĚMCOVÁ, Lukáš SMITAL a Marie NOVÁKOVÁ. ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study. \textit{Scientific Reports}. LONDON: NATURE PUBLISHING GROUP, 2017, roč.~7, č.~11239, s.~1-11. ISSN~2045-2322. Dostupné z: https://dx.doi.org/10.1038/s41598-017-10942-6.

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