Redefining Performance Evaluation Tools for Real-Time QRS Complex Classification Systems

International audienceIn a heartbeat classification procedure, the detection of QRS complex waveforms is necessary. In many studies, this heartbeat extraction function is not considered: the inputs of the classifier are assumed to be correctly identified. This paper aims to redefine classical performance evaluation tools in entire QRS complex classification systems and to evaluate the effects induced by QRS detection errors on the performance of a heartbeat classification processing (normal vs abnormal). Performance statistics are given and discussed considering the MIT/BIH database records that are replayed on a real-time classification system imposed of the classical detector proposed by Hamilton & Tompkins, followed by a neural network classifier. This study shows that a classification accuracy of 96.72% falls to 94.90% when a drop of 1.78% error rate is introduced in the detector quality. This corresponds an increase of about 50% bad classifications