Comput Methods Programs Biomed - ECG beat classification using a cost sensitive classifier.

Tópicos

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Resumo

In this paper, we introduce a new system for ECG beat classification using Support Vector Machines (SVMs) classifier with rejection. After ECG preprocessing, the QRS complexes are detected and segmented. A set of features including frequency information, RR intervals, QRS morphology and AC power of QRS detail coefficients is exploited to characterize each beat. An SVM follows to classify the feature vectors. Our decision rule uses dynamic reject thresholds following the cost of misclassifying a sample and the cost of rejecting a sample. Significant performance enhancement is observed when the proposed approach is tested with the MIT-BIH arrhythmia database. The achieved results are represented by the average accuracy of 97.2% with no rejection and 98.8% for the minimal classification cost.

Resumo Limpo

paper introduc new system ecg beat classif use support vector machin svms classifi reject ecg preprocess qrs complex detect segment set featur includ frequenc inform rr interv qrs morpholog ac power qrs detail coeffici exploit character beat svm follow classifi featur vector decis rule use dynam reject threshold follow cost misclassifi sampl cost reject sampl signific perform enhanc observ propos approach test mitbih arrhythmia databas achiev result repres averag accuraci reject minim classif cost

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