Multi-scale transitions of fuzzy sample entropy of RR-intervals and their phase-randomized surrogates: A possibility to diagnose congestive heart failure. (January 2017)

• Record Type: Journal Article
• Authors: von Tscharner, Vinzenz; Zandiyeh, Payam
• Is Part Of: Biomedical signal processing and control. Volume 31(2017)
• Page Start: 350
• Page End: 356
• Publication Date: 2017-01
• Subjects: Surrogate analysis -- Fuzzy sample entropy -- Heart rate variability -- Multi scale transition -- Regularity -- Complexity; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2016.08.014
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The complexity and regularity of a signal is defined. It was shown that the information about the control of the heart rate variability is largely encoded in the phase of the Fourier transformed signal. Distinct differences were observed in the biphasic multi-scale transition transitions between controls and patients suffering from congestive heart failure. The differences between normalized fSEn surrogates and the RR-signal show a clear fast response lasting about 3–4 s followed by a long lasting trend. The average of the outlined differences classified patients and controls with a sensitivity of 87% and specificity of 89%. Abstract: Heart rate variability (HRV) is the variability of consecutive cardiac inter-beat intervals (RR-intervals or RR-signals). It contains features that are essential indicators for the health of a human being. Recently, researchers have investigated the usefulness of non-linear dynamics to gain insight on temporal relationships within the RR-signals. Sample entropy and fuzzy sample entropy (fSEn) are variables reflecting the non-linear dynamics of HRV. However, previous studies have only rarely considered that the information of a signal might be encoded in its phase. In this article, we define and quantify complexity of a signal by the part of regularity caused by the non-random aspect of the phase of a signal. The purpose of the study is to show that information about the control of HRV is largely encoded in the phase of the Fourier transformed signal and its complexity can be quantified using the multi scale transition (MST) of fSEn of RR-signals and their phase-randomized surrogates. This may allow classifying individual participants with congestive heart failure from healthy controls. The results show distinct differences in the biphasic MST transitions between controls and patients suffering from congestive heart failure. The differences of normalized fSEn of surrogates minus fSEn of the RR-signal show a clear first, fast response lasting about 3–4 s followed by a long lasting trend. The sum of these differences in the fast response trend represent a feature variable that allowed classifying patients and controls with a high sensitivity of 87% and a high specificity of 89%. The relationship to neural control of the HRV can now be investigated with variables that reflect the regularity and complexity of the HRV using the information that is encoded in the phase of the Fourier transformed RR-signal and is not resolved by the classical, power spectra based HRV analysis.