The Performance of Short-Term Heart Rate Variability in the Detection of Congestive Heart FailureReport as inadecuate




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BioMed Research International - Volume 2016 2016, Article ID 1675785, 11 pages -

Research Article

Universidade CEUMA, No. 100, 65903-093 Imperatriz, MA, Brazil

Laboratory for Biological Information Processing, Universidade Federal do Maranhão, S-N, São Luís, MA, Brazil

Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya-shi 464-8603, Japan

Received 2 March 2016; Revised 13 June 2016; Accepted 26 July 2016

Academic Editor: Said Audi

Copyright © 2016 Fausto Lucena et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Congestive heart failure CHF is a cardiac disease associated with the decreasing capacity of the cardiac output. It has been shown that the CHF is the main cause of the cardiac death around the world. Some works proposed to discriminate CHF subjects from healthy subjects using either electrocardiogram ECG or heart rate variability HRV from long-term recordings. In this work, we propose an alternative framework to discriminate CHF from healthy subjects by using HRV short-term intervals based on 256 RR continuous samples. Our framework uses a matching pursuit algorithm based on Gabor functions. From the selected Gabor functions, we derived a set of features that are inputted into a hybrid framework which uses a genetic algorithm and -nearest neighbour classifier to select a subset of features that has the best classification performance. The performance of the framework is analyzed using both Fantasia and CHF database from Physionet archives which are, respectively, composed of 40 healthy volunteers and 29 subjects. From a set of nonstandard 16 features, the proposed framework reaches an overall accuracy of 100% with five features. Our results suggest that the application of hybrid frameworks whose classifier algorithms are based on genetic algorithms has outperformed well-known classifier methods.





Author: Fausto Lucena, Allan Kardec Barros, and Noboru Ohnishi

Source: https://www.hindawi.com/



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