Classification of Arrhythmias Using Heart Rate Variability Signals and the Prediction by Partial Matching Algorithm

• Thiago Fernandes Lins de Medeiros UFPE
• Amanda Barreto Cavalcanti UFPB
• Berg Élisson Sampaio Cavalcante UFPB
• Erick Vagner Cabral de Lima Borges UFPB
• Igor Lucena Peixoto Andrezza UFPB
• Leonardo Vidal Batista UFPB

Abstract

This paper describes a method of heart arrhythmia classification based on the signal of the heart frequency variability (HFV) and the compression algorithm Prediction by Partial Matching The extraction of the HFV signal is performed by analyzing the electrocardiogram to detect the R peak from the QRS complex of the heartbeats. Then it is possible to generate the signal. The classification of the heart arrhythmias is done in two steps. In the learning stage the PPM algorithm builds statistic models for the extracted tachogram. In the classification stage, the tachograms are compressed by the models obtained and attributed to the class whose models obtain the best ratio of compression.

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