A method for arrhythmic episode classification in ECGs using fuzzy logic and Markov models

A method for arrhythmic episode classification using only the RR-interval signal is presented. The method is based on fuzzy logic and Markov models, while classification is performed for nine categories of cardiac rhythms. A two-stage classifier is applied. In the first stage, a fuzzy system classifies the episode using the mean value and standard deviation of the RR-intervals. In the second, the RR-interval signal is transformed to character sequences, which are classified by Markov models. Two representation techniques are used for the extraction of the character sequences: symbolic dynamics and one based on the RR-interval length. The classification of an episode is achieved combining the outcomes of the two stages. The MIT-BIH arrhythmia database is used for the evaluation of the proposed method. The obtained results indicate high performance (accuracy 73%) in arrhythmic episode classification.