Next Article in Journal
Confidential Cooperative Communication with the Trust Degree of Jammer
Next Article in Special Issue
Quantifying the Multiscale Predictability of Financial Time Series by an Information-Theoretic Approach
Previous Article in Journal
Gearbox Fault Diagnosis Based on Hierarchical Instantaneous Energy Density Dispersion Entropy and Dynamic Time Warping
Previous Article in Special Issue
On the Statistical Properties of Multiscale Permutation Entropy: Characterization of the Estimator’s Variance
Open AccessArticle

On the Robustness of Multiscale Indices for Long-Term Monitoring in Cardiac Signals

1
Department of Signal Theory and Communications, Telematics and Computing Systems, Rey Juan Carlos University, 28933 Fuenlabrada, Spain
2
GECOS Lab, ENSA, Cadi Ayyad University, 40000 Marrakech, Morocco
3
Center for Computational Simulation, Universidad Politécnica de Madrid, 28040 Pozuelo, Spain
4
Hospital Clínico Universitario Virgen de la Arrixaca de Murcia, 30120 Murcia, Spain
*
Author to whom correspondence should be addressed.
Entropy 2019, 21(6), 594; https://doi.org/10.3390/e21060594
Received: 6 April 2019 / Revised: 6 June 2019 / Accepted: 14 June 2019 / Published: 15 June 2019
(This article belongs to the Special Issue Multiscale Entropy Approaches and Their Applications)
  |  
PDF [3578 KB, uploaded 15 June 2019]
  |  

Abstract

The identification of patients with increased risk of Sudden Cardiac Death (SCD) has been widely studied during recent decades, and several quantitative measurements have been proposed from the analysis of the electrocardiogram (ECG) stored in 1-day Holter recordings. Indices based on nonlinear dynamics of Heart Rate Variability (HRV) have shown to convey predictive information in terms of factors related with the cardiac regulation by the autonomous nervous system, and among them, multiscale methods aim to provide more complete descriptions than single-scale based measures. However, there is limited knowledge on the suitability of nonlinear measurements to characterize the cardiac dynamics in current long-term monitoring scenarios of several days. Here, we scrutinized the long-term robustness properties of three nonlinear methods for HRV characterization, namely, the Multiscale Entropy (MSE), the Multiscale Time Irreversibility (MTI), and the Multifractal Spectrum (MFS). These indices were selected because all of them have been theoretically designed to take into account the multiple time scales inherent in healthy and pathological cardiac dynamics, and they have been analyzed so far when monitoring up to 24 h of ECG signals, corresponding to about 20 time scales. We analyzed them in 7-day Holter recordings from two data sets, namely, patients with Atrial Fibrillation and with Congestive Heart Failure, by reaching up to 100 time scales. In addition, a new comparison procedure is proposed to statistically compare the poblational multiscale representations in different patient or processing conditions, in terms of the non-parametric estimation of confidence intervals for the averaged median differences. Our results show that variance reduction is actually obtained in the multiscale estimators. The MSE (MTI) exhibited the lowest (largest) bias and variance at large scales, whereas all the methods exhibited a consistent description of the large-scale processes in terms of multiscale index robustness. In all the methods, the used algorithms could turn to give some inconsistency in the multiscale profile, which was checked not to be due to the presence of artifacts, but rather with unclear origin. The reduction in standard error for several-day recordings compared to one-day recordings was more evident in MSE, whereas bias was more patently present in MFS. Our results pave the way of these techniques towards their use, with improved algorithmic implementations and nonparametric statistical tests, in long-term cardiac Holter monitoring scenarios. View Full-Text
Keywords: nonlinear dynamics; multiscale indices; cardiac risk stratification; Holter; long term monitoring; multiscale entropy; multifractal spectrum; multiscale time irreversibility nonlinear dynamics; multiscale indices; cardiac risk stratification; Holter; long term monitoring; multiscale entropy; multifractal spectrum; multiscale time irreversibility
Figures

Figure 1

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 (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

El-Yaagoubi, M.; Goya-Esteban, R.; Jabrane, Y.; Muñoz-Romero, S.; García-Alberola, A.; Rojo-Álvarez, J.L. On the Robustness of Multiscale Indices for Long-Term Monitoring in Cardiac Signals. Entropy 2019, 21, 594.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top