Počet záznamů: 1  

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

  1. 1.
    0482149 - ÚPT 2018 RIV GB eng J - Článek v odborném periodiku
    Maršánová, L. - Ronzhina, M. - Smíšek, Radovan - Vítek, M. - Němcová, A. - Smítal, L. - Nováková, M.
    ECG features and methods for automatic classification of ventricular premature and ischemic heartbeats: A comprehensive experimental study.
    Scientific Reports. Roč. 7, SEP (2017), s. 1-11, č. článku 11239. ISSN 2045-2322. E-ISSN 2045-2322
    Grant CEP: GA ČR GAP102/12/2034
    Institucionální podpora: RVO:68081731
    Klíčová slova: stress-induced ischemia * ECG * arrhytmias
    Obor OECD: Medical engineering
    Impakt faktor: 4.122, rok: 2017
    http://www.nature.com/articles/s41598-017-10942-6

    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 percent and 96.2 percent 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 percent and 93.5 percent, respectively).
    Trvalý link: http://hdl.handle.net/11104/0277550

     
     
Počet záznamů: 1  

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