Three-class ECG beat classification by ordinal entropies

Abstract The automatic and rapid analysis of long-term electrocardiogram (ECG) records still remains a challenging task. Most of the existing algorithms are time consuming and require a training step. In this paper, we present a training free two-level hierarchical model based on ordinal patterns for classifying ECG beats into three types. The classification rules include morphological and temporal properties of the ECG signal that are compared to R-R and QRS dependent thresholds derived from the beat CEOP or PE series. The experimental classification rates obtained from the MIT-BIH Arrhythmia database (93.66%) and the St. Petersburg Institute of Cardiological Technics (INCART) database (95.43%), considering the Advancement of Medical Instrumentation (AAMI) recommendations, confirm the ability of the proposed approach for a multi-class classification.