TY - GEN
T1 - Highly accurate ECG beat classification based on continuous wavelet transformation and multiple support vector machine classifiers
AU - Zellmer, Erik
AU - Shang, Fei
AU - Zhang, Hao
PY - 2009
Y1 - 2009
N2 - This paper presents a highly accurate ECG beat classification system. It uses continuous wavelet transformation combined with time domain morphology analysis to form three separate feature vectors from each beat. Each of these feature vectors are then used separately to train three different support vector machine (SVM) classifiers. During data classification each of the three classifiers independently classifies each beat; with the result of the multi classifier based classification system being decided by voting among the three independent classifiers. Using this method the multi classifier based system is able to reach an average accuracy of 99.72% in the classification of six types of beats. This accuracy is higher than the individual accuracy of any of the participating SVM classifiers as well as higher than previously presented ECG beat classification systems showing the effectiveness of the technique.
AB - This paper presents a highly accurate ECG beat classification system. It uses continuous wavelet transformation combined with time domain morphology analysis to form three separate feature vectors from each beat. Each of these feature vectors are then used separately to train three different support vector machine (SVM) classifiers. During data classification each of the three classifiers independently classifies each beat; with the result of the multi classifier based classification system being decided by voting among the three independent classifiers. Using this method the multi classifier based system is able to reach an average accuracy of 99.72% in the classification of six types of beats. This accuracy is higher than the individual accuracy of any of the participating SVM classifiers as well as higher than previously presented ECG beat classification systems showing the effectiveness of the technique.
KW - Continuous wavelet transformation
KW - ECG beat classification
KW - Support vector machine
UR - http://www.scopus.com/inward/record.url?scp=74049088277&partnerID=8YFLogxK
U2 - 10.1109/BMEI.2009.5305280
DO - 10.1109/BMEI.2009.5305280
M3 - Conference contribution
AN - SCOPUS:74049088277
SN - 9781424441341
T3 - Proceedings of the 2009 2nd International Conference on Biomedical Engineering and Informatics, BMEI 2009
BT - Proceedings of the 2009 2nd International Conference on Biomedical Engineering and Informatics, BMEI 2009
T2 - 2009 2nd International Conference on Biomedical Engineering and Informatics, BMEI 2009
Y2 - 17 October 2009 through 19 October 2009
ER -