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

• Published in: Scientific reports Vol. 7; no. 1; pp. 11239 - 11
• Main Authors: Maršánová, Lucie, Ronzhina, Marina, Smíšek, Radovan, Vítek, Martin, Němcová, Andrea, Smital, Lukas, Nováková, Marie
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 11-09-2017; Nature Publishing Group
• Subjects: 639/166/985; 639/705/531; 692/4019/2773; Animal models; Animals; Automatic classification; Automation - methods; Classification; Data Analysis; EKG; Electrocardiography; Electrocardiography - methods; Heart; Heart diseases; Heart Diseases - diagnosis; Humanities and Social Sciences; Ischemia; Morphology; multidisciplinary; Rabbits; Science; Science (multidisciplinary); Ventricle
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/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% 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).