-
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,
Jazyk angličtina Země Anglie, Velká Británie
Typ dokumentu časopisecké články, práce podpořená grantem
NLK
Directory of Open Access Journals
od 2011
Free Medical Journals
od 2011
Nature Open Access
od 2011-12-01
PubMed Central
od 2011
Europe PubMed Central
od 2011
ProQuest Central
od 2011-01-01
Open Access Digital Library
od 2011-01-01
Open Access Digital Library
od 2011-01-01
Health & Medicine (ProQuest)
od 2011-01-01
ROAD: Directory of Open Access Scholarly Resources
od 2011
Springer Nature OA/Free Journals
od 2011-12-01
- MeSH
- analýza dat MeSH
- automatizace metody MeSH
- elektrokardiografie metody MeSH
- králíci MeSH
- nemoci srdce diagnóza MeSH
- zvířata MeSH
- Check Tag
- králíci MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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