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
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