Paper Title :Support Vector Machine Based Heartbeat Classification
Author :N P Joshi, P S Topannavar
Article Citation :N P Joshi ,P S Topannavar ,
(2014 ) " Support Vector Machine Based Heartbeat Classification " ,
International Journal of Advances in Science, Engineering and Technology(IJASEAT) ,
pp. 54-58,
Volume-2,Issue-3
Abstract : In this paper, a new approach for heartbeat classification is proposed. The system uses the combination of
morphological and dynamic features of ECG signal. Morphological features extracted using Wavelet transform and
independent component analysis (ICA). Each heartbeat undergoes both the techniques separately. The dynamic features
extracted are RR interval features. Support vector machine is used as a classifier, after concatenating the results of both the
feature extraction techniques, to classify the heartbeat signals into 16 classes.Whole process is applied to both the lead
signals and then the classifier results are fused to make final decision about the classification. The overall accuracy in
classifying the signals from MIT-BIH arrhythmia database should be 99% in “class-oriented” evaluation and an accuracy of
86% in the “subject-oriented” evaluation.
Type : Research paper
Published : Volume-2,Issue-3
DOIONLINE NO - IJASEAT-IRAJ-DOIONLINE-1048
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Copyright: © Institute of Research and Journals
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Published on 2014-07-09 |
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