ECG beat classification using a cost sensitive classifier

Z Zidelmal; A Amirou; D Ould-Abdeslam; J Merckle

doi:10.1016/j.cmpb.2013.05.011

ECG beat classification using a cost sensitive classifier

Comput Methods Programs Biomed. 2013 Sep;111(3):570-7. doi: 10.1016/j.cmpb.2013.05.011. Epub 2013 Jul 10.

Authors

Z Zidelmal¹, A Amirou, D Ould-Abdeslam, J Merckle

Affiliation

¹ Laboratoire LAMPA, Université Mouloud Mammeri, Tizi-Ouzou, Algeria. z-zidelmal@mail.ummto.dz

PMID: 23849928
DOI: 10.1016/j.cmpb.2013.05.011

Abstract

In this paper, we introduce a new system for ECG beat classification using Support Vector Machines (SVMs) classifier with rejection. After ECG preprocessing, the QRS complexes are detected and segmented. A set of features including frequency information, RR intervals, QRS morphology and AC power of QRS detail coefficients is exploited to characterize each beat. An SVM follows to classify the feature vectors. Our decision rule uses dynamic reject thresholds following the cost of misclassifying a sample and the cost of rejecting a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database. The achieved results are represented by the average accuracy of 97.2% with no rejection and 98.8% for the minimal classification cost.

Keywords: Classification cost; ECG beat classification; Support Vector Machines (SVMs).

MeSH terms

Costs and Cost Analysis*
Electrocardiography*
Humans
Models, Theoretical
Support Vector Machine