Using Support Vector Machines (SVMs) with Reject Option for Heartbeat Classification

Abstract

In this paper, we introduce a new system for ECG beat classification using Support Vector Machines (SVMs) classifier with a double hinge loss. This classifier has the option to reject samples that cannot be classified with enough confidence. Specifically in medical diagnoses, the risk of a wrong classification is so high that it is convenient to reject the sample. After ECG preprocessing, feature selection and extraction, our decision rule uses dynamic reject thresholds following the cost of rejecting a sample and the cost of misclassifying a sample. Significant performance enhancement is observed when the proposed approach was tested with the MIT/BIH arrythmia database. The achieved results are represented by the error reject tradeoff and a sensitivity higher than 99%, being competitive to other published studies.