Detail
Článek
Článek online
FT
Medvik - BMČ
  • Je něco špatně v tomto záznamu ?

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

L. Maršánová, M. Ronzhina, R. Smíšek, M. Vítek, A. Němcová, L. Smital, M. Nováková,

. 2017 ; 7 (1) : 11239. [pub] 20170911

Jazyk angličtina Země Anglie, Velká Británie

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/bmc19028749

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).

Citace poskytuje Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc19028749
003      
CZ-PrNML
005      
20250507093655.0
007      
ta
008      
190813s2017 enk f 000 0|eng||
009      
AR
024    7_
$a 10.1038/s41598-017-10942-6 $2 doi
035    __
$a (PubMed)28894131
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a enk
100    1_
$a Maršánová, Lucie $u Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic. xmarsa08@stud.feec.vutbr.cz.
245    10
$a ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study / $c L. Maršánová, M. Ronzhina, R. Smíšek, M. Vítek, A. Němcová, L. Smital, M. Nováková,
520    9_
$a 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).
650    _2
$a zvířata $7 D000818
650    _2
$a automatizace $x metody $7 D001331
650    _2
$a analýza dat $7 D000078332
650    _2
$a elektrokardiografie $x metody $7 D004562
650    _2
$a nemoci srdce $x diagnóza $7 D006331
650    _2
$a králíci $7 D011817
655    _2
$a časopisecké články $7 D016428
655    _2
$a práce podpořená grantem $7 D013485
700    1_
$a Ronzhina, Marina $u Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic.
700    1_
$a Smíšek, Radovan $u Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic. Institute of Scientific Instruments, The Czech Academy of Sciences, Královopolská 147, Brno, 612 64, Czech Republic.
700    1_
$a Vítek, Martin $u Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic.
700    1_
$a Němcová, Andrea $u Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic. $7 xx0331813
700    1_
$a Smital, Lukas $u Department of Biomedical Engineering, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technická 12, Brno, 616 00, Czech Republic.
700    1_
$a Nováková, Marie $u Department of Physiology, Faculty of Medicine, Masaryk University, Kamenice 5, Brno, 625 00, Czech Republic.
773    0_
$w MED00182195 $t Scientific reports $x 2045-2322 $g Roč. 7, č. 1 (2017), s. 11239
856    41
$u https://pubmed.ncbi.nlm.nih.gov/28894131 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y a $z 0
990    __
$a 20190813 $b ABA008
991    __
$a 20250507093653 $b ABA008
999    __
$a ok $b bmc $g 1433898 $s 1067209
BAS    __
$a 3
BAS    __
$a PreBMC
BMC    __
$a 2017 $b 7 $c 1 $d 11239 $e 20170911 $i 2045-2322 $m Scientific reports $n Sci Rep $x MED00182195
LZP    __
$a Pubmed-20190813

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...