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Multi-stage classification of congestive heart failure based on short-term heart rate variability

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  • Isler, Yalcin
  • Narin, Ali
  • Ozer, Mahmut
  • Perc, Matjaž

Abstract

In this study, we propose an automatic system to diagnose congestive heart failure using short-term heart rate variability analysis. The system involves a multi-stage classifier. The features of heart rate variability are computed from time-domain and frequency-domain measures through power spectral density estimations of different transform methods. Nonlinear heart rate variability measures are also calculated by using Poincare plot, symbolic dynamics, detrended fluctuation analysis, and sample entropy. Different combinations of heart rate variability features are selected according to their statistical significance levels and then applied to the classifier. The first two stages of the classifier consist of simple perceptron classifiers that are trained by a genetic algorithm. Five different classifiers, namely k-nearest neighbors, linear discriminant analyses, multilayer perceptron, support vector machines, and radial basis function artificial neuronal network, are tested for the third stage. The proposed system results in a classification performance of an accuracy of 98.8%, specificity of 98.1%, and sensitivity of 100%. We show that our approach provides an effective and computationally efficient tool to automatically diagnose congestive heart failure patients.

Suggested Citation

  • Isler, Yalcin & Narin, Ali & Ozer, Mahmut & Perc, Matjaž, 2019. "Multi-stage classification of congestive heart failure based on short-term heart rate variability," Chaos, Solitons & Fractals, Elsevier, vol. 118(C), pages 145-151.
  • Handle: RePEc:eee:chsofr:v:118:y:2019:i:c:p:145-151
    DOI: 10.1016/j.chaos.2018.11.020
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    References listed on IDEAS

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    1. Wenhui Chen & Lianrong Zheng & Kunyang Li & Qian Wang & Guanzheng Liu & Qing Jiang, 2016. "A Novel and Effective Method for Congestive Heart Failure Detection and Quantification Using Dynamic Heart Rate Variability Measurement," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-18, November.
    2. Narin, Ali & Isler, Yalcin & Ozer, Mahmut & Perc, Matjaž, 2018. "Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 56-65.
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    Cited by:

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