Redefining performance evaluation tools for real-time QRS complex classification systems

In 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 communication 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 heartbeat classification processing (normal versus abnormal). Performance statistics are given and discussed considering the MIT/BIH database records that are replayed on a real-time classification system composed of the classical detector proposed by Hamilton and 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 to an increase of about 50% bad classifications.