@inproceedings{326da5ec350243b684988bd02ae9af01,
title = "Neuro-fuzzy-based arrhythmia classification using heart rate variability features",
abstract = "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.",
keywords = "Arrhythmia, Artificial Neural Networks, Fuzzy Logic, Heart Rate Variability, Support Vector Machines",
author = "Felipe Ram{\'i}rez and H{\'e}ctor Allende-Cid and Alejandro Veloz and H{\'e}ctor Allende",
year = "2010",
doi = "10.1109/SCCC.2010.38",
language = "English",
isbn = "9780769544007",
series = "Proceedings - International Conference of the Chilean Computer Science Society, SCCC",
publisher = "IEEE Computer Society",
pages = "205--211",
booktitle = "Proceedings - 29th International Conference of the Chilean Computer Science Society, SCCC 2010",
}