Heart Rate Variability on 7-Day Holter Monitoring Using a Bootstrap Rhythmometric Procedure

Goya–Esteban, Rebeca; Mora-Jiménez, Inmaculada; Rojo‐Álvarez, José Luis; Barquero-Pérez, Óscar; Pastor‐Pérez, Francisco J.; Manzano‐Fernández, Sergio; Pascual-Figal, Domingo A.; García‐Alberola, Arcadi

doi:10.1109/tbme.2010.2040899

Cited by 21 publications

(15 citation statements)

References 31 publications

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“…The paired bootstrap hypothesis test determines that the addition of a new sinusoidal component is relevant when at least 97.5% of the B values, for the estimated probability density function of ∆E k , are on the right-hand side of zero; a detailed explanation is found in [7].…”

Section: Rhythmometric Analysismentioning

confidence: 99%

A Multicentric Study of Long:term Rhythm Patterns in Heart Rate

Goya–Esteban

Barquero-Pérez

Alzueta

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Heart rate (HR) shows oscillations with different periods as a result of the sympatho-vagal balance. The most studied ones are short-period variations and the circadian pattern. However, the existence of rhythms of longer periods has not been systematically studied.The aim of this work is to study HR long-period rhythms in a multicentric database including 336 patients in sinus rhythm, with implanted cardioverter defibrillator. We used two methodological approaches: First a rhythmometric procedure to automatically select the statistically significant rhythms present in the signal; Second, the LASSO path approach to analyze the order of activation of the rhythms, representing the importance of each rhythm.Most of the population showed a significant annual rhythm (78% day/80% night). Weekly and quarterly rhythms were also present (weekly 26% /26% ; quarterly 22%/21%). Monthly rhythm was rarely present. Most present rhythm combinations were annual plus weekly (21%/21%) and annual plus quarterly (19%/18%). The order of activation given by the LASSO path was in agreement with the multicomponent rhythmometric model in the 86%/85% of the cases.The unusual long monitoring period, and the high number of patients, represent an ideal scenario to robustly assess the existence of long-term rhythms.

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Section: Rhythmometric Analysismentioning

confidence: 99%

A Multicentric Study of Long:term Rhythm Patterns in Heart Rate

Goya–Esteban

Barquero-Pérez

Alzueta

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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“…16 This rhythmometric analysis considers not only the circadian rhythm corresponding to 24 h (24 H), but also the ultradian rhythms corresponding to 8 h (8 H) and the infradian rhythms corresponding to 7D. The method leans on a bootstrap 17 hypothesis test to automatically select the rhythm components with statistical relevance for each patient.…”

Section: Rhythmometric Analysismentioning

confidence: 99%

Prognostic Significance of Long-Period Heart Rate Rhythms in Chronic Heart Failure

Pastor‐Pérez

Manzano‐Fernández

Goya–Esteban

et al. 2012

Circ J

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“…This possible inconsistency may be due to the fact that traditional algorithms are based on single scale analysis, and they can not take into account the complex temporal fluctuations inherent to healthy physiologic control systems. It is usual that studies based on 1-day Holter monitoring [11] envision that relevant information could be obtained from longer duration recordings, however, few studies [12,13] have scrutinized nonlinear indices in several-day Holter monitoring, despite its current and increasing availability in the clinical practice. Note in the following that, whereas some authors refer to long-term Holter as those with duration about 24 h, we will use long-term to refer to the Holter recordings when measured for several days throughout this paper.…”

Section: Introductionmentioning

confidence: 99%

“…This method can be used either for establishing comparisons among patients or subjects with different conditions, or to scrutinize the impact of preprocessing or data length on the statistical properties of the multiscale representations. The procedure can be seen as an extension of previously used statistical comparisons [20,21] in terms of nonparametric bootstrap tests for confidence bands [12,29].…”

Section: Introductionmentioning

confidence: 99%

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

El-Yaagoubi

Goya–Esteban

Jabrane

et al. 2019

Entropy

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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.

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Heart Rate Variability on 7-Day Holter Monitoring Using a Bootstrap Rhythmometric Procedure

Cited by 21 publications

References 31 publications

A Multicentric Study of Long:term Rhythm Patterns in Heart Rate

A Multicentric Study of Long:term Rhythm Patterns in Heart Rate

Prognostic Significance of Long-Period Heart Rate Rhythms in Chronic Heart Failure

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

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