ECG identification of arrhythmias by using an associative Petri net

Changes in the normal rhythm of a human heart may result in different cardiac arrhythmias, which may be immediately fatal or cause irreparable damage to the heart sustained over long periods of time. Therefore, the ability to automatically identify arrhythmias from ECG recordings is important for clinical diagnosis and treatment. In this study, classifier by using associative Petri net for personalized ECG arrhythmias pattern identification is proposed. Association production rules and reasoning algorithm of APN are created for ECG arrhythmias detection. The performance of our approach compares well with previously reported results and could be a part of monitoring system for the detection of ECG arrhythmias.