Classification of Arrhythmia using Multi-Class Support Vector Machine

Tonghui Li; Jieming Ma; Xinyu Pan; Yujia Zhai; Ka Lok Man

Classification of Arrhythmia using Multi-Class Support Vector Machine

Tonghui Li, Jieming Ma, Xinyu Pan, Yujia Zhai, Ka Lok Man

Department of Computing

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

7 Citations (Scopus)

Abstract

Arrhythmia has become the most common disease in the medical field. Manual diagnosis of arrhythmia beats is very tedious owing to its nonlinear and complex nature of electrocardiogram (ECG). In this article, a multi-class support vector machine (MSVM) based approach is proposed to solve ECG multi-classification problem. Based on the characteristics of the R-R interval, it has the capability of detecting normal heart rate (NOR), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature complex (APC) and Ventricular premature beat (VPC) was mainly discussed. Using ECG MIT-BIH database, simulation results show the proposed method achieves a very high classification accuracy.

Original language	English
Title of host publication	Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017
Editors	Oscar Castillo, S. I. Ao, Craig Douglas, David Dagan Feng, A. M. Korsunsky
Publisher	Newswood Limited
Pages	710-713
Number of pages	4
ISBN (Electronic)	9789881404770
Publication status	Published - 2017
Event	2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017 - Hong Kong, Hong Kong Duration: 15 Mar 2017 → 17 Mar 2017

Publication series

Name	Lecture Notes in Engineering and Computer Science
Volume	2228
ISSN (Print)	2078-0958

Conference

Conference	2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017
Country/Territory	Hong Kong
City	Hong Kong
Period	15/03/17 → 17/03/17

Keywords

Arrhythmia
Classification
ECG
Kernel function
MSVM

Cite this

Li, T., Ma, J., Pan, X., Zhai, Y., & Man, K. L. (2017). Classification of Arrhythmia using Multi-Class Support Vector Machine. In O. Castillo, S. I. Ao, C. Douglas, D. D. Feng, & A. M. Korsunsky (Eds.), Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017 (pp. 710-713). (Lecture Notes in Engineering and Computer Science; Vol. 2228). Newswood Limited.

Li, Tonghui ; Ma, Jieming ; Pan, Xinyu et al. / Classification of Arrhythmia using Multi-Class Support Vector Machine. Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017. editor / Oscar Castillo ; S. I. Ao ; Craig Douglas ; David Dagan Feng ; A. M. Korsunsky. Newswood Limited, 2017. pp. 710-713 (Lecture Notes in Engineering and Computer Science).

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title = "Classification of Arrhythmia using Multi-Class Support Vector Machine",

abstract = "Arrhythmia has become the most common disease in the medical field. Manual diagnosis of arrhythmia beats is very tedious owing to its nonlinear and complex nature of electrocardiogram (ECG). In this article, a multi-class support vector machine (MSVM) based approach is proposed to solve ECG multi-classification problem. Based on the characteristics of the R-R interval, it has the capability of detecting normal heart rate (NOR), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature complex (APC) and Ventricular premature beat (VPC) was mainly discussed. Using ECG MIT-BIH database, simulation results show the proposed method achieves a very high classification accuracy.",

keywords = "Arrhythmia, Classification, ECG, Kernel function, MSVM",

author = "Tonghui Li and Jieming Ma and Xinyu Pan and Yujia Zhai and Man, {Ka Lok}",

note = "Funding Information: The authors would like to thank Suzhou Electrical Apparatus Science Research Institute Co., Ltd (China) for providing the materials and computer facilities necessary in achieving the results reported. This research is supported by the Natural Science Foundation of Jiangsu Province (Grant No. BK20160355), the Natural Science Research Project of Higher Education of Jiangsu (Grant No. 15KJB480002), and the Science and Technology Project of Ministry of Housing and Urban-Rural Development (Grant No. 2016-K1-019).; 2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017 ; Conference date: 15-03-2017 Through 17-03-2017",

year = "2017",

language = "English",

series = "Lecture Notes in Engineering and Computer Science",

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pages = "710--713",

editor = "Oscar Castillo and Ao, {S. I.} and Craig Douglas and Feng, {David Dagan} and Korsunsky, {A. M.}",

booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017",

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Li, T, Ma, J, Pan, X, Zhai, Y & Man, KL 2017, Classification of Arrhythmia using Multi-Class Support Vector Machine. in O Castillo, SI Ao, C Douglas, DD Feng & AM Korsunsky (eds), Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017. Lecture Notes in Engineering and Computer Science, vol. 2228, Newswood Limited, pp. 710-713, 2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017, Hong Kong, Hong Kong, 15/03/17.

Classification of Arrhythmia using Multi-Class Support Vector Machine. / Li, Tonghui; Ma, Jieming; Pan, Xinyu et al.
Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017. ed. / Oscar Castillo; S. I. Ao; Craig Douglas; David Dagan Feng; A. M. Korsunsky. Newswood Limited, 2017. p. 710-713 (Lecture Notes in Engineering and Computer Science; Vol. 2228).

Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review

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AU - Li, Tonghui

AU - Ma, Jieming

AU - Pan, Xinyu

AU - Zhai, Yujia

AU - Man, Ka Lok

N1 - Funding Information: The authors would like to thank Suzhou Electrical Apparatus Science Research Institute Co., Ltd (China) for providing the materials and computer facilities necessary in achieving the results reported. This research is supported by the Natural Science Foundation of Jiangsu Province (Grant No. BK20160355), the Natural Science Research Project of Higher Education of Jiangsu (Grant No. 15KJB480002), and the Science and Technology Project of Ministry of Housing and Urban-Rural Development (Grant No. 2016-K1-019).

PY - 2017

Y1 - 2017

N2 - Arrhythmia has become the most common disease in the medical field. Manual diagnosis of arrhythmia beats is very tedious owing to its nonlinear and complex nature of electrocardiogram (ECG). In this article, a multi-class support vector machine (MSVM) based approach is proposed to solve ECG multi-classification problem. Based on the characteristics of the R-R interval, it has the capability of detecting normal heart rate (NOR), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature complex (APC) and Ventricular premature beat (VPC) was mainly discussed. Using ECG MIT-BIH database, simulation results show the proposed method achieves a very high classification accuracy.

AB - Arrhythmia has become the most common disease in the medical field. Manual diagnosis of arrhythmia beats is very tedious owing to its nonlinear and complex nature of electrocardiogram (ECG). In this article, a multi-class support vector machine (MSVM) based approach is proposed to solve ECG multi-classification problem. Based on the characteristics of the R-R interval, it has the capability of detecting normal heart rate (NOR), left bundle branch block (LBBB), right bundle branch block (RBBB), atrial premature complex (APC) and Ventricular premature beat (VPC) was mainly discussed. Using ECG MIT-BIH database, simulation results show the proposed method achieves a very high classification accuracy.

KW - Arrhythmia

KW - Classification

KW - ECG

KW - Kernel function

KW - MSVM

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T3 - Lecture Notes in Engineering and Computer Science

SP - 710

EP - 713

BT - Proceedings of the International MultiConference of Engineers and Computer Scientists 2017, IMECS 2017

A2 - Castillo, Oscar

A2 - Ao, S. I.

A2 - Douglas, Craig

A2 - Feng, David Dagan

A2 - Korsunsky, A. M.

PB - Newswood Limited

T2 - 2017 International MultiConference of Engineers and Computer Scientists, IMECS 2017

Y2 - 15 March 2017 through 17 March 2017

ER -

Classification of Arrhythmia using Multi-Class Support Vector Machine

Abstract

Publication series

Conference

Keywords

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