ECG Beat Classifier Using Support Vector Machine

被引：0
|
作者：
Besrour, R. [1 ]
Lachiri, Z. [1 ]
Ellouze, N. [1 ]
机构：
[1] ENIT, Dept Elect, Signal Image & Pattern Recognit Res Unit, Tunis 1002, Tunisia
来源：
2008 3RD INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES: FROM THEORY TO APPLICATIONS, VOLS 1-5 | 2008年
关键词：
Classification; Arrhythmia; Support Vector Machine; morphological descriptors; High Order Statistic;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper introduces a new method of heartbeat classification based on the support vector machine classifier using morphological descriptors and High Order Statistic using MIT/BIH Arrhythmia database. Using the morphological descriptors and polynomial kernel, we have obtained an average sensitivity equal to 89,92% and an average specificity about 82,45%, and in the case of Gaussian kernel, we have obtained an average sensitivity equal to 94,26% and an average specificity about 79,02%. Using the High Order Statistic and polynomial kernel, we have obtained an average sensitivity equal to 95,86% and an average specificity about 90,20%, and in the case of Gaussian kernel, we have obtained an average sensitivity equal to 97,15% and an average specificity about 93,07%. The association of the two parameters increases the averages of classification rates; so the sensitivity is 98,38% and the specificity to 94,87% with polynomial kernel and respectively about 94,43% et 95,81% with Gaussian kernel.
引用
收藏
页码：816 / 820
页数：5
相关论文
共 50 条
  • [31] Highly Accurate ECG Beat Classification based on Continuous Wavelet Transformation and Multiple Support Vector Machine Classifiers
    Zellmer, Erik
    Shang, Fei
    Zhang, Hao
    PROCEEDINGS OF THE 2009 2ND INTERNATIONAL CONFERENCE ON BIOMEDICAL ENGINEERING AND INFORMATICS, VOLS 1-4, 2009, : 641 - 645
  • [32] Using Ambiguity Measure Feature Selection Algorithm for Support Vector Machine Classifier
    Mengle, Saket S. R.
    Goharian, Nazli
    APPLIED COMPUTING 2008, VOLS 1-3, 2008, : 916 - 920
  • [33] Speaker identification using hybrid neural network support vector machine classifier
    Karthikeyan V.
    Priyadharsini S.S.
    Balamurugan K.
    Ramasamy M.
    International Journal of Speech Technology, 2022, 25 (4) : 1041 - 1053
  • [34] An ensemble support vector machine classifier for health identification using tongue image
    Cui, Yan
    Wang, Hongwu
    Liao, Shizhong
    Journal of Computational Information Systems, 2014, 10 (22): : 9649 - 9656
  • [35] A pedestrian detector using histograms of oriented gradients and a Support Vector Machine classifier
    Bertozzi, M.
    Broggi, A.
    Del Rose, M.
    Felisa, M.
    Rakotomamonjy, A.
    Suard, E.
    2007 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE, VOLS 1 AND 2, 2007, : 397 - +
  • [36] Robust Arrhythmia Classifier Using Wavelet Transform and Support Vector Machine Classification
    Chia, Nyoke Goon
    Hau, Yuan Wen
    Jamaludin, Mohd Najeb
    2017 IEEE 13TH INTERNATIONAL COLLOQUIUM ON SIGNAL PROCESSING & ITS APPLICATIONS (CSPA), 2017, : 243 - 248
  • [37] Classification of microarray using MapReduce based proximal support vector machine classifier
    Kumar, Mukesh
    Rath, Santanu Kumar
    KNOWLEDGE-BASED SYSTEMS, 2015, 89 : 584 - 602
  • [38] Monitoring of drill runout using Least Square Support Vector Machine classifier
    Mary, Susai J.
    Balaji, Sai M. A.
    Krishnakumari, A.
    Nakandhrakumar, R. S.
    Dinakaran, D.
    MEASUREMENT, 2019, 146 : 24 - 34
  • [39] Recognition of welding defects in radiographic images by using support vector machine classifier
    Wang, Xin
    Wong, Brian Stephen
    Tan, ChingSeong
    Research Journal of Applied Sciences, Engineering and Technology, 2010, 2 (03) : 295 - 301
  • [40] Classification of Hard Exudates in Fundus Images using Support Vector Machine Classifier
    Vijila, S. Antelin
    Rajesh, R. S.
    RESEARCH JOURNAL OF PHARMACEUTICAL BIOLOGICAL AND CHEMICAL SCIENCES, 2015, 6 (03): : 440 - 445
12345