Authors: Igor Lucena Peixoto Andrezza; Erick Vagner Cabral De Lima Borges; Leonardo Vidal Batista
Addresses: Center of Informatics, Federal University of Paraiba, Joao Pessoa, Paraiba, 58051–900, Brazil ' Center of Informatics, Federal University of Paraiba, Joao Pessoa, Paraiba, 58051–900, Brazil ' Center of Informatics, Federal University of Paraiba, Joao Pessoa, Paraiba, 58051–900, Brazil
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.
Keywords: electrocardiograms; ECG signals; HRV; heart rate variability; PPM; prediction by partial matching; cardiac arrhythmia classification; atrial fibrillation; second heart block; normal sinus rhythm; premature ventricular contraction; ventricular fibrillation; sinus bradycardia; statistical modelling; tachograms.
International Journal of Computer Applications in Technology, 2015 Vol.52 No.4, pp.285 - 291
Available online: 13 Dec 2015 *Full-text access for editors Access for subscribers Purchase this article Comment on this article