ECG arrhythmia classification using time frequency distribution techniques.
10.1007/s13534-017-0043-2
- Author:
Safa SULTAN QURRAIE
1
;
Rashid GHORBANI AFKHAMI
Author Information
1. Faculty of Electrical and Computer Engineering, University of Tabriz, 29 Bahman Blvd., Tabriz, Iran. safa.sultanqurraie@tabrizu.ac.ir
- Publication Type:Original Article
- Keywords:
Cardiac arrhythmia;
Classification;
Decision tree;
Ensemble learner;
Time-frequency analysis;
Wigner-Ville distribution
- MeSH:
Arrhythmias, Cardiac*;
Classification*;
Decision Trees;
Electrocardiography*;
Methods
- From:
Biomedical Engineering Letters
2017;7(4):325-332
- CountryRepublic of Korea
- Language:English
-
Abstract:
In this paper, we focus on classifying cardiac arrhythmias. The MIT-BIH database is used with 14 original classes of labeling which is then mapped into 5 more general classes, using the Association for the Advancement of Medical Instrumentation standard. Three types of features were selected with a focus on the time-frequency aspects of ECG signal. After using the Wigner-Ville distribution the time-frequency plane is split into 9 windows considering the frequency bandwidth and time duration of ECG segments and peaks. The summation over these windows are employed as pseudo-energy features in classification. The “subject-oriented” scheme is used in classification, meaning the train and test sets include samples from different subjects. The subject-oriented method avoids the possible overfitting issues and guaranties the authenticity of the classification. The overall sensitivity and positive predictivity of classification is 99.67 and 98.92%, respectively, which shows a significant improvement over previous studies.