Multi-stage classification of congestive heart failure based on short-term heart rate variability. (January 2019)

• Record Type: Journal Article
• Authors: Isler, Yalcin; Narin, Ali; Ozer, Mahmut; Perc, Matjaž
• Is Part Of: Chaos, solitons and fractals. Volume 118(2019)
• Page Start: 145
• Page End: 151
• Publication Date: 2019-01
• Subjects: Frequency-domain measure -- Nonlinear variability -- Heart rate variability -- Congestive heart failure -- Multi-stage classifier -- Genetic algorithm; Chaotic behavior in systems -- Periodicals; Solitons -- Periodicals; Fractals -- Periodicals; Chaotic behavior in systems; Fractals; Solitons; Periodicals; 003.7
• DOI: 10.1016/j.chaos.2018.11.020
• Languages: English
• ISSNs: 0960-0779

Abstract

Highlights: Short-term heart rate variability is analyzed for diagnosing congestive heart failure. Our method allows an early diagnosis of congestive heart failure. Feature selection dominates the classification performance. Our method provides an improved classification performance. 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.