Phase-space reconstruction of electrocardiogram for heartbeat classification

Abstract

Heartbeat classification is crucial for cardiac arrhythmia analysis. QRS complex presents important characteristics which are beneficial to distinguish abnormal beats from normal beats. In the present study we propose a novel descriptor for QRS complex. The waveform is transformed to a two-dimensional phase space and then mapped to a one-dimensional portrait partition area (PPA). The proposed morphological descriptor has advantages of no need to detect Q and S characteristic points, tolerating R-peak misalignment and taking into account temporal relation of data samples. On the basis of 32 records from the MIT/BIH arrhythmia database, normal QRS and premature ventricular contraction (PVC) beats show different phase space portraits and PPA. An artificial neuronal network using PPA as the input feature was built for heartbeat classification. Our results showed that the sensitivity and specificity of distinguishing PVC from normal QRS achieved 0.9699 and 0.9651 in the testing sets, respectively.