Sample Entropy in Electrocardiogram During Atrial Fibrillation

Horie, Takuya; Burioka, Naoto; Amisaki, Takashi; Shimizu, Eiji

doi:10.33160/yam.2018.03.007

Cited by 15 publications

(9 citation statements)

References 31 publications

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“…Horie used a data acquisition frequency of F s = 200 Hz, and the results of entropy SampEn were compared with τ = 1 and τ = 5. In both cases, SampEn was different between patients with AF and without arrhythmia [ 27 ]. Based on these findings, it could be interpreted that the entropy values remained stable with changes in τ .…”

Section: Discussionmentioning

confidence: 99%

Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators

Espinosa

Talero

Weinstein

2020

Entropy

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Electrocardiography (ECG) and electroencephalography (EEG) signals provide clinical information relevant to determine a patient’s health status. The nonlinear analysis of ECG and EEG signals allows for discovering characteristics that could not be found with traditional methods based on amplitude and frequency. Approximate entropy (ApEn) and sampling entropy (SampEn) are nonlinear data analysis algorithms that measure the data’s regularity, and these are used to classify different electrophysiological signals as normal or pathological. Entropy calculation requires setting the parameters r (tolerance threshold), m (immersion dimension), and τ (time delay), with the last one being related to how the time series is downsampled. In this study, we showed the dependence of ApEn and SampEn on different values of τ, for ECG and EEG signals with different sampling frequencies (Fs), extracted from a digital repository. We considered four values of Fs (128, 256, 384, and 512 Hz for the ECG signals, and 160, 320, 480, and 640 Hz for the EEG signals) and five values of τ (from 1 to 5). We performed parametric and nonparametric statistical tests to confirm that the groups of normal and pathological ECG and EEG signals were significantly different (p < 0.05) for each F and τ value. The separation between the entropy values of regular and irregular signals was variable, demonstrating the dependence of ApEn and SampEn with Fs and τ. For ECG signals, the separation between the conditions was more robust when using SampEn, the lowest value of Fs, and τ larger than 1. For EEG signals, the separation between the conditions was more robust when using SampEn with large values of Fs and τ larger than 1. Therefore, adjusting τ may be convenient for signals that were acquired with different Fs to ensure a reliable clinical classification. Furthermore, it is useful to set τ to values larger than 1 to reduce the computational cost.

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Section: Discussionmentioning

confidence: 99%

Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators

Espinosa

Talero

Weinstein

2020

Entropy

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“…Thus, the time scales exhibiting near-1/ f fluctuations were reported to reveal new information regarding the complexity of HRV, which could be measured by MSE analysis [ 10 ]. To evaluate the irregularity of ECG signals in the 1/ f fluctuation area and the onset of a disease, sample entropy is considered to be an effective complex system analysis [ 27 ]. Furthermore, Ho et al [ 28 ] also reported that the sample entropy calculated in the 1/ f fluctuation area may serve as a significant predictor of mortality in patients with congestive heart failure.…”

Section: Discussionmentioning

confidence: 99%

Ischemic Stroke Risk Assessment by Multiscale Entropy Analysis of Heart Rate Variability in Patients with Persistent Atrial Fibrillation

Chairina

Yoshino

Kiyono

et al. 2021

Entropy

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It has been recognized that heart rate variability (HRV), defined as the fluctuation of ventricular response intervals in atrial fibrillation (AFib) patients, is not completely random, and its nonlinear characteristics, such as multiscale entropy (MSE), contain clinically significant information. We investigated the relationship between ischemic stroke risk and HRV with a large number of stroke-naïve AFib patients (628 patients), focusing on those who had never developed an ischemic/hemorrhagic stroke before the heart rate measurement. The CHA2DS2−VASc score was calculated from the baseline clinical characteristics, while the HRV analysis was made from the recording of morning, afternoon, and evening. Subsequently, we performed Kaplan–Meier method and cumulative incidence function with mortality as a competing risk to estimate the survival time function. We found that patients with sample entropy (SE(s)) ≥ 0.68 at 210 s had a significantly higher risk of an ischemic stroke occurrence in the morning recording. Meanwhile, the afternoon recording showed that those with SE(s) ≥ 0.76 at 240 s and SE(s) ≥ 0.78 at 270 s had a significantly lower risk of ischemic stroke occurrence. Therefore, SE(s) at 210 s (morning) and 240 s ≤ s ≤ 270 s (afternoon) demonstrated a statistically significant predictive value for ischemic stroke in stroke-naïve AFib patients.

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“…Other studies used complexity measures (e.g. entropy measures) to characterize and detect AF episodes from the inter-beat tachogram [23] or raw ECG [24].…”

Section: Discussionmentioning

confidence: 99%

Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation

Gadhoumi

Badilini

et al. 2018

Journal of Electrocardiology

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Sample Entropy in Electrocardiogram During Atrial Fibrillation

Cited by 15 publications

References 31 publications

Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators

Effects of Tau and Sampling Frequency on the Regularity Analysis of ECG and EEG Signals Using ApEn and SampEn Entropy Estimators

Ischemic Stroke Risk Assessment by Multiscale Entropy Analysis of Heart Rate Variability in Patients with Persistent Atrial Fibrillation

Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation

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