ECG beat classification using a cost sensitive classifier

被引：60

作者：

Zidelmal, Z. ^{[1
]}

Amirou, A. ^{[1
]}

Ould-Abdeslam, D. ^{[1
]}

Merckle, J. ^{[1
]}

机构：

[1] Univ Mouloud Mammeri, Lab LAMPA, Tizi Ouzou, Algeria

来源：

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE | 2013年 / 111卷 / 03期

关键词：

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

D O I：

10.1016/j.cmpb.2013.05.011

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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. (c) 2013 Elsevier Ireland Ltd. All rights reserved.

引用

下载

页码：570 / 577

页数：8

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