Neuro-fuzzy-based Arrhythmia Classification Using Heart Rate Variability Features

Arrhythmia diagnosis is commonly conducted through visual analysis of human electrocardiograms, a very resource consuming task for physicians. In this paper we present a computational approach for arrhythmia detection based on heart rate variability signal analysis and the application of a neuro-fuzzy classification model called SONFIS. The aforementioned method generates a set of linguistically interpretable inference rules for pattern classification and outperforms artificial neural networks and support vector machines in accuracy and several other performance indicators.