Heart arrhythmia classification using the prediction by partial matching algorithm

Abstract

This paper describes a method for heart arrhythmia classification based on the heart rate variability HRV signal and the compression algorithm PPM. The arrhythmias to be identified are: atrial fibrillation, second heart block, normal sinus rhythm, premature ventricular contraction, ventricular fibrillation and sinus bradycardia. In the learning stage the PPM algorithm builds statistical models for the extracted tachogram. In the classification stage, the tachograms are compressed by the obtained models and attributed to the class whose models results in the best compression rate. The tests were performed with 1367 segments from the MIT-BIH Arrhythmia Database and Creighton University Ventricular Tachyarrhythmia Database. The classifier was tested for several context sizes and different training/classification sets. The performance of the classifier was measured according to sensitivity, specificity and accuracy, obtaining 96.93, 99.47 and 98.73% respectively, with the context size equal to one. These results are comparable to those of the best modern classifiers.