Vector quantization for ECG beats classification

Tong Liu, Yujuan Si, Dunwei Wen, Mujun Zang, Weiwei Song, Liuqi Lang

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

6 Citations (Scopus)

Abstract

Reducing the feature dimensionality can improve the computational efficiency of electrocardiogram (ECG) beats classification system. In the long term ECG classification task, vector quantization has demonstrated its advantage in both dimensionality reduction and accuracy increase, but the existing vector quantization methods are not capable of representing the difference of each waveform among ECG beats. To make vector quantization available for ECG beats classification, in this paper, we propose a strategy that aligns each wave of all beats, and then build a dictionary corresponding to each wave segment. Thus vector quantization can distinguish each waveform of different beats. We compare our method with the popular beats features such as sampling point feature, fast Fourier transform feature, and discrete wavelet transform feature. The classification results show that our feature has high accuracy and is capable of reducing computational complexity of beats classification system, which demonstrate that the proposed method can provide an effective vector quantization feature for beats classification.

Original languageEnglish
Title of host publicationProceedings - 17th IEEE International Conference on Computational Science and Engineering, CSE 2014, Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014
EditorsXingang Liu, Didier El Baz, Ching-Hsien Hsu, Kai Kang, Weifeng Chen
Pages13-20
Number of pages8
ISBN (Electronic)9781479979813
DOIs
Publication statusPublished - 26 Jan. 2015
Event17th IEEE International Conference on Computational Science and Engineering, CSE 2014 - Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014 - Chengdu, China
Duration: 19 Dec. 201421 Dec. 2014

Publication series

NameProceedings - 17th IEEE International Conference on Computational Science and Engineering, CSE 2014, Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014

Conference

Conference17th IEEE International Conference on Computational Science and Engineering, CSE 2014 - Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014
Country/TerritoryChina
CityChengdu
Period19/12/1421/12/14

Keywords

  • Classification
  • ECG beats
  • Feature extraction
  • K-means
  • Vector quantization

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