Heart arrhythmia classification using the PPM algorithm

This paper describes a method of heart arrhythmia classification based on the heart rate variability (HRV) signal and the compression algorithm Prediction by Partial Matching. The arrhythmias to be identified are: Normal Sinus Rhythm, Premature Ventricular Contraction, 2^nd Heart Block and Sinus Bradycardia. The extraction of the HRV signal is performed by analyzing the electrocardiogram to detect the R peak from the QRS complex of the heartbeats, and then generating the signal. The classification of the arrhythmias is done in two steps. 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 1558 segments from the MIT-BIH Arrhythmia Database. The classifier was tested for several context sizes, k, and different training/classification sets. The performance of the classifier was measured according to sensitivity, specificity and accuracy. The best results were obtained when context size was equal to two (k = 2), achieving 91,74% of sensitivity, 99,37% of specificity and 99,14% of accuracy, results comparable to those of the best modern classifiers.