Advanced computation in cardiovascular physiology: new challenges and opportunities

Valenza, Gaetano; Faes, Luca; Toschi, Nicola; Barbieri, Riccardo

doi:10.1098/rsta.2020.0265

Cited by 4 publications

(5 citation statements)

References 16 publications

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“…It has been known that the classification between CHF or AF disorder from a normal condition is most important [1], [44]. Generally, MFDE has a superior ability to differentiate between cardiovascular patients (CHF, AF) and the healthy than MSE and MDE.…”

Section: ) Statistical Analysis Of Experiments Resultsmentioning

confidence: 99%

“…1) The univariate time series 𝐱𝐱 = {𝑥𝑥 1 , 𝑥𝑥 2 , … , 𝑥𝑥 𝑁𝑁 } is normalized to time series 𝐲𝐲 = {𝑦𝑦 1 , 𝑦𝑦 2 , … , 𝑦𝑦 𝑁𝑁 } by the normal cumulative distribution function (NCDF) as in (1). The components of the series 𝐲𝐲 have real values from 0 to 1.…”

Section: A Dispersion Entropymentioning

confidence: 99%

“…Electrocardiogram (ECG) is a medical diagnostic tool that measures and records the electrical activity of the heart [1], [2], [3]. Heart rate variability (HRV), which is the variation of inter-beat intervals between consecutive heartbeats, namely RR intervals, reflects the mechanism that is controlled by the autonomic nervous system (ANS) [4].…”

Section: Introductionmentioning

confidence: 99%

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Quantifying Heart Rate Variability Using Multiscale Fuzzy Dispersion Entropy

Kim,

Choi

2024

IEEE Access

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Heart rate variability (HRV), which is the variation of inter-beat intervals, exhibits complex characteristics on multiple temporal scales due to the balancing function of the autonomic nervous system. Although there are various nonlinear analysis methods for assessing the complexity of HRV, quantifying HRV over multiple scales is lacking. Here, we present a novel multiscale fuzzy dispersion entropy (MFDE) measure that incorporates quantifying fuzzy dispersion entropy over multiple temporal scales. The proposed MFDE comprises two steps: First, a coarse-graining procedure is carried out for the multiscale decomposition of an inter-beat interval. Second, it conducts FDE computation for each coarse-grained time series. It results in the quantification of complexity, reflecting the long-range correlations inherent in HRV. Using synthetic signals and actual electrocardiogram (ECG), we evaluate the performance of MFDE and compare it to the traditional multiscale entropy methods. The results using synthetic signals show better robustness of MFDE for quantifying complexity with various lengths and predefined parameters. The results using ECGs demonstrate that the proposed MFDE leads to more significant discrimination of HRVs of different cardiovascular states regarding p-values from the Mann-Whitney U test. The capability of MFDE can provide a prospective tool for real-time and practical computer-aided diagnosis using HRV analysis.INDEX TERMS Heart rate variability, RR intervals, Complexity, Multiscale fuzzy dispersion entropy.

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Section: ) Statistical Analysis Of Experiments Resultsmentioning

confidence: 99%

Section: A Dispersion Entropymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quantifying Heart Rate Variability Using Multiscale Fuzzy Dispersion Entropy

Kim,

Choi

2024

IEEE Access

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“…Due to the capability of MSE, it has been widely used in diverse applications including biomedical environments [28][29][30][31][32][33]. Unlike other applications, there are certain things to consider when utilizing cardiovascular signals [34,35]. The ability to accurately and quantitatively determine the meaning of a signal in a short amount of time is essential.…”

Section: Discussionmentioning

confidence: 99%

Multiscale Cumulative Residual Dispersion Entropy with Applications to Cardiovascular Signals

Kim,

Choi

2023

Entropy

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Heart rate variability (HRV) is used as an index reflecting the adaptability of the autonomic nervous system to external stimuli and can be used to detect various heart diseases. Since HRVs are the time series signal with nonlinear property, entropy has been an attractive analysis method. Among the various entropy methods, dispersion entropy (DE) has been preferred due to its ability to quantify the time series’ underlying complexity with low computational cost. However, the order between patterns is not considered in the probability distribution of dispersion patterns for computing the DE value. Here, a multiscale cumulative residual dispersion entropy (MCRDE), which employs a cumulative residual entropy and DE estimation in multiple temporal scales, is presented. Thus, a generalized and fast estimation of complexity in temporal structures is inherited in the proposed MCRDE. To verify the performance of the proposed MCRDE, the complexity of inter-beat interval obtained from ECG signals of congestive heart failure (CHF), atrial fibrillation (AF), and the healthy group was compared. The experimental results show that MCRDE is more capable of quantifying physiological conditions than preceding multiscale entropy methods in that MCRDE achieves more statistically significant cases in terms of p-value from the Mann–Whitney test.

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“…The electrocardiogram (ECG) is a crucial diagnostic tool that measures and records the electrical activity of the heart. An ECG is typically measured using electrodes on the chest, arms, and legs [1,2]. ECG signals can be easily measured and thus are a helpful non-invasive tool.…”

Section: Introductionmentioning

confidence: 99%

Detection of Ventricular Fibrillation Using Ensemble Empirical Mode Decomposition of ECG Signals

Oh,

Choi

2024

Electronics

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Ventricular fibrillation (VF) is a critical ventricular arrhythmia with severe consequences. Due to the severity of VF, it urgently requires a rapid and accurate detection of abnormal patterns in ECG signals. Here, we present an efficient method to detect abnormal electrocardiogram (ECG) signals associated with VF by measuring orthogonality between intrinsic mode functions (IMFs) derived from a data-driven decomposition method, namely, ensemble empirical mode decomposition (EEMD). The proposed method incorporates the decomposition of the ECG signal into its IMFs using EEMD, followed by the computation of the angles between subsequent IMFs, especially low-order IMFs, as the features to discriminate normal and abnormal ECG patterns. The proposed method was validated through experiments using a public MIT-BIH ECG dataset for its effectiveness in detecting VF ECG signals compared to conventional methods. The proposed method achieves a sensitivity of 99.22%, a specificity of 99.37%, and an accuracy of 99.28% with a 3 s ECG window and a support vector machine (SVM) with a linear kernel, which performs better than existing VF detection methods. The capability of the proposed method can provide a perspective approach for the real-time and practical computer-aided diagnosis of VF.

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Advanced computation in cardiovascular physiology: new challenges and opportunities

Cited by 4 publications

References 16 publications

Quantifying Heart Rate Variability Using Multiscale Fuzzy Dispersion Entropy

Quantifying Heart Rate Variability Using Multiscale Fuzzy Dispersion Entropy

Multiscale Cumulative Residual Dispersion Entropy with Applications to Cardiovascular Signals

Detection of Ventricular Fibrillation Using Ensemble Empirical Mode Decomposition of ECG Signals

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