Cardiac arrhythmia classification using the phase space sorted by Poincare sections

Many methods for automatic heartbeat classification have been applied and reported in literature, but methods, which used the basin geometry of quasi-periodic oscillations of electrocardiogram (ECG) signal in the phase space for classifying cardiac arrhythmias, frequently extracted a limited amount of information of this geometry. Therefore, in this study, we proposed a novel technique based on Poincare section to quantify the basin of quasi-periodic oscillations, which can fill the mentioned gap to some extent. For this purpose, we first reconstructed the two-dimensional phase space of ECG signal. Then, we sorted this space using the Poincare sections in different angles. Finally, we evaluated the geometric features extracted from the sorted spaces of five heartbeat groups recommend by the association for the advancement of medical instrumentation (AAMI) by using the sequential forward selection (SFS) algorithm. The results of this algorithm indicated that a combination of nine features extracted from the sorted phase space along with per and post instantaneous heart rate could significantly separate the five heartbeat groups (99.23% and 96.07% for training and testing sets, respectively). Comparing these results with the results of earlier work also indicated that our proposed method had a figure of merit (FOM) about 32.12%. Therefore, this new technique not only can quantify the basin geometry of quasi-periodic oscillations of ECG signal in the phase space, but also its output can improve the performance of detection systems developed for the cardiac arrhythmias, especially in the five heartbeat groups recommend by the AAMI.