Evaluation of monofractal and multifractal properties of inter‐beat (R‐R) intervals in cardiac signals for differentiation between the normal and pathology classes. Issue 9 (1st December 2019)

• Record Type: Journal Article
• Authors: Gorshkov, Oleg; Ombao, Hernando
• Is Part Of: IET signal processing. Volume 13:Issue 9(2019)
• Page Start: 798
• Page End: 805
• Publication Date: 2019-12-01
• Subjects: medical signal processing -- statistical testing -- fluctuations -- electrocardiography -- regression analysis -- medical disorders -- fractals; multifractal properties -- multifractal detrended fluctuation analysis -- formal statistical tests -- cardiac signals -- congestive heart failure -- atrial fibrillation -- monofractal properties -- healthy groups -- pathological groups -- DMA method -- Kruskal‐Wallis test -- interbeat intervals -- sudden death syndrome -- electrocardiogram -- logistic regression model; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2018.5536
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : In this study, the monofractal and multifractal properties of inter‐beat (R‐R) intervals in cardiac signals for normal and pathology classes were studied and applied to a dataset in PhysioNet which consists of 24 h R‐R intervals from 54 healthy subjects (hs) and 92 patients with various diagnoses [44 with congestive heart failure (chf), 25 with atrial fibrillation (af) and 23 diagnosed with sudden death syndrome (sd)]. The results in this study indicate that the most suitable method for estimating the monofractal properties of R‐R intervals is detrending moving average (DMA). The Hurst exponents ( H ) of the healthy and pathological groups, calculated using the DMA method, are shown to be statistically different by the Kruskal–Wallis test [ p‐value < 0.01; healthy H hs = 0.23(0.18–0.27); pathological group H chf = 0.06(0.03–0.10), H af = 0.05(0.03–0.07), H sd = 0.05(0.03–0.08)]. To study the multifractal properties of R‐R intervals, the multifractal detrended fluctuation analysis was used. Formal statistical tests indicate statistically significant differences ( p‐value < 0.05, ANOVA and pairwise testing) between the width of the spectrum of the chf group W chf = 1.11 ± 0.12 and the width of the spectra of the af group W af = 0.58 ± 0.15, the sd group W sd = 0.60 ± 0.20, and the hs group W hs = 0.61 ± 0.08. Using these results, a logistic regression model was developed for differentiating chf from other pathologies (af and sd).