A NOVEL FRAMEWORK FOR CARDIAC ARRHYTHMIA CLASSIFICATION BASED ON SUBSPACE PROJECTION AND DECISION TREE

Year 2020, , 350 - 365, 15.06.2020

Semih Ergin , Selcan Kaplan Berkaya , Alper Kürşat Uysal , Efnan Şora Günal , Serkan Gunal , Mehmet Bilginer Gülmezoğlu

https://doi.org/10.18038/estubtda.630634

Abstract

Cardiac arrhythmia basically refers to
abnormal activity of heart. Correct classification of cardiac arrhythmia is
therefore crucial for appropriate treatment of heart diseases. In this paper, a
novel approach is proposed for cardiac arrhythmia classification. Initially,
the feature vectors extracted from raw electrocardiogram (ECG) signals are
projected into a particular subspace obtained via the Common Vector Approach,
which is an effective subspace method. The projected vectors are then fed into
two distinct decision-tree-based classifiers—namely, C4.5 and random forest.
The results obtained from the proposed approach are compared with those
obtained with the original feature vectors using the same classifiers. For this
purpose, the well-known MIT-BIH arrhythmia database was utilized. Six different
sets of features based on QRS, time-domain, wavelet transform and power
spectral density are derived from ECG signals in this database. The feature
sets are then used in the classification of five main beat types including
non-ectopic, ventricular ectopic, supraventricular ectopic, fusion and unknown.
The experimental results reveal that the recognition performances achieved by
most of the projected features are explicitly higher than those obtained with
the original ones. In addition, the classification accuracy of the proposed
approach climbs to 100% for the test set.

Keywords

Cardiac arrhythmia classification, electrocardiography, feature selection, subspace projection, decision tree

Supporting Institution

Eskiehir Osmangazi University

Project Number

201215037

Thanks

This work was supported by Eskisehir Osmangazi University, Fund of Scientific Research Projects under grant number 201215037.

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