Patient adaptable ventricular arrhythmia classifier using template matching

In this paper we propose a real-time method for discrimination of ventricular ectopic and normal beats in the electrocardiogram (ECG). The heartbeat waveforms were evaluated within a fixed-length window around the fiducial points (100 ms before, 100 ms after) after being normalized. Our algorithm was designed to operate with no expert assistance; the operator is not required to initially select any known beat templates which makes it applicable in real-time. It is based on the dominancy of normal beats during the first several seconds for most of records which mostly match the real cases. In addition to R-R intervals, we extract other features as beat width, P wave existence for each beat. Also we apply template matching between the learned template and the unknown beat where template beats are created for each beat shape on runtime. Template matching not only classifies normal dominant beat but also multi-form ventricular ectopic beats where each form is classified separately so as doctors and medical stuff could take the better decision. Our proposed algorithm was tested on MIT-BIH ECG database records with normal and ventricular ectopic beat classes defined in AAMI standard while other records are excluded. Our results show 97.24% overall accuracy with 98.93% and 94.54% sensitivities for normal and ventricular ectopic beats respectively.