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Heartbeat classification using decision level fusion

Heartbeat classification using decision level fusion Biomed Eng Lett (2014) 4:388-395 DOI 10.1007/s13534-014-0158-7 ORIGINAL ARTICLE Zhancheng Zhang and Xiaoqing Luo Received: 13 January 2014 / Revised: 21 October 2014 / Accepted: 28 October 2014 © The Korean Society of Medical & Biological Engineering and Springer 2014 Abstract ConclusionsThe proposed method demonstrates better PurposeAutomatic heartbeat classification is an important performance than the existing fusion methods. technique to assist doctors to identify ectopic heartbeats in long-term ECG recording. In this paper, we employed a Keywords Heartbeat classification, Support vector machine, multi-lead fused classification schema to improve the Decision fusion, Multi-lead performance of heartbeat classification. Methods In this paper, we introduce a multi-lead fused classification schema, in which a multi-class heartbeat INTRODUCTION classification task is decomposed into a serials of one-versus- one (OvO) support vector machine (SVM) binary classifiers, Electrocardiogram (ECG) is a noninvasive, inexpensive and then the corresponding OvO binary classifiers of all leads are well-established diagnostic tool. It is widely used to evaluate fused based on the decision score of each binary classifier, heart function. However, for the analysis of long-term ECG the final label is predicted by voting the fused OvO recording, beat-by-beat manual examination is tedious and classifiers. The ECG features adopted include inter-beat http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biomedical Engineering Letters Springer Journals

Heartbeat classification using decision level fusion

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References (22)

Publisher
Springer Journals
Copyright
Copyright © 2014 by Korean Society of Medical and Biological Engineering and Springer
Subject
Engineering; Biomedical Engineering; Biophysics and Biological Physics; Biomedicine general; Medical and Radiation Physics
ISSN
2093-9868
eISSN
2093-985X
DOI
10.1007/s13534-014-0158-7
Publisher site
See Article on Publisher Site

Abstract

Biomed Eng Lett (2014) 4:388-395 DOI 10.1007/s13534-014-0158-7 ORIGINAL ARTICLE Zhancheng Zhang and Xiaoqing Luo Received: 13 January 2014 / Revised: 21 October 2014 / Accepted: 28 October 2014 © The Korean Society of Medical & Biological Engineering and Springer 2014 Abstract ConclusionsThe proposed method demonstrates better PurposeAutomatic heartbeat classification is an important performance than the existing fusion methods. technique to assist doctors to identify ectopic heartbeats in long-term ECG recording. In this paper, we employed a Keywords Heartbeat classification, Support vector machine, multi-lead fused classification schema to improve the Decision fusion, Multi-lead performance of heartbeat classification. Methods In this paper, we introduce a multi-lead fused classification schema, in which a multi-class heartbeat INTRODUCTION classification task is decomposed into a serials of one-versus- one (OvO) support vector machine (SVM) binary classifiers, Electrocardiogram (ECG) is a noninvasive, inexpensive and then the corresponding OvO binary classifiers of all leads are well-established diagnostic tool. It is widely used to evaluate fused based on the decision score of each binary classifier, heart function. However, for the analysis of long-term ECG the final label is predicted by voting the fused OvO recording, beat-by-beat manual examination is tedious and classifiers. The ECG features adopted include inter-beat

Journal

Biomedical Engineering Letters – Springer Journals

Published: Dec 4, 2014

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