ECG beat classification using a cost sensitive classifier
作者： Z. Zidelmal , A. Amirou , D. Ould-Abdeslam , J. Merckle
DOI: 10.1016/J.CMPB.2013.05.011
关键词: Beat (acoustics) 、 Support vector machine 、 Decision rule 、 Classifier (UML) 、 Feature vector 、 Pattern recognition 、 Speech recognition 、 Computer science 、 Artificial intelligence
摘要: In this paper, we introduce a new system for ECG beat classification using Support Vector Machines (SVMs) classifier with rejection. After preprocessing, the QRS complexes are detected and segmented. A set of features including frequency information, RR intervals, morphology AC power detail coefficients is exploited to characterize each beat. An SVM follows classify feature vectors. Our decision rule uses dynamic reject thresholds following cost misclassifying sample rejecting sample. Significant performance enhancement observed when proposed approach tested MIT-BIH arrhythmia database. The achieved results represented by average accuracy 97.2% no rejection 98.8% minimal cost.
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