Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants Compared to Full-Length Long Signals

Borin, Airton Monte Serrat; Humeau-Heurtier, Anne; Silva, Luiz Eduardo Virgílio; Murta, Leonardo

doi:10.3390/e23121620

Cited by 10 publications

(4 citation statements)

References 27 publications

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“…In other words, it means that the energy of 1/f noise contains complex dynamics that vary depending on frequency. Through analysis of multiscale based entropy, it appears that 1/f noise is a more complex structure than WGN [9], [27], [32], [33]. Multiscale entropy values for the synthetic signals, WGN and 1/f noise, provide important insight with respect to physiological complexity.…”

Section: Evaluation Signals a Synthetic Signalsmentioning

confidence: 99%

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: Evaluation Signals a Synthetic Signalsmentioning

confidence: 99%

Quantifying Heart Rate Variability Using Multiscale Fuzzy Dispersion Entropy

Kim,

Choi

2024

IEEE Access

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“…The cross-entropy loss function, also regarded as log loss, is the most commonly used loss function for back propagation. The cross-entropy loss function increases as the predicted probability deviates from the actual label, and can be described as follows L ce ŷi , y i = − ∑ i y i log ŷi (25) In this paper, the label l n of each image is used, which is only assumed to be 1 for images belonging to the same class of images during testing, and 0 otherwise. The crossentropy formula can be expressed as…”

Section: Proposed Network With Rmee Criterionmentioning

confidence: 99%

“…Previously, Chen et al proposed researches focusing on maximum correntropy theory and minimum error entropy criteria to improve the robustness of machine learning theory [20][21][22]. In addition, a series of entropy-based learning algorithms have been presented to deal with the robustness improvement of machine learning models, including guided complement entropy and fuzzy entropy [23][24][25]. Nevertheless, there is no application of the ITL-based approach in the spike-based continual meta-learning to improve its learning robustness.…”

Section: Introductionmentioning

confidence: 99%

Robust Spike-Based Continual Meta-Learning Improved by Restricted Minimum Error Entropy Criterion

Yang

Tan

Chen

2022

Entropy

142

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The spiking neural network (SNN) is regarded as a promising candidate to deal with the great challenges presented by current machine learning techniques, including the high energy consumption induced by deep neural networks. However, there is still a great gap between SNNs and the online meta-learning performance of artificial neural networks. Importantly, existing spike-based online meta-learning models do not target the robust learning based on spatio-temporal dynamics and superior machine learning theory. In this invited article, we propose a novel spike-based framework with minimum error entropy, called MeMEE, using the entropy theory to establish the gradient-based online meta-learning scheme in a recurrent SNN architecture. We examine the performance based on various types of tasks, including autonomous navigation and the working memory test. The experimental results show that the proposed MeMEE model can effectively improve the accuracy and the robustness of the spike-based meta-learning performance. More importantly, the proposed MeMEE model emphasizes the application of the modern information theoretic learning approach on the state-of-the-art spike-based learning algorithms. Therefore, in this invited paper, we provide new perspectives for further integration of advanced information theory in machine learning to improve the learning performance of SNNs, which could be of great merit to applied developments with spike-based neuromorphic systems.

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“…Finally, the set of HRV indices explored here, although comprehensive, is not extensive. The prognostic value of several other entropy [ 58 – 60 ], fractal [ 61 ] and general [ 62 – 64 ] methods in CCC should be investigated in future studies.…”

Section: Limitationsmentioning

confidence: 99%

Heart rate variability as a biomarker in patients with Chronic Chagas Cardiomyopathy with or without concomitant digestive involvement and its relationship with the Rassi score

et al. 2022

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Background Dysautonomia plays an ancillary role in the pathogenesis of Chronic Chagas Cardiomyopathy (CCC), but is the key factor causing digestive organic involvement. We investigated the ability of heart rate variability (HRV) for death risk stratification in CCC and compared alterations of HRV in patients with isolated CCC and in those with the mixed form (CCC + digestive involvement). Thirty-one patients with CCC were classified into three risk groups (low, intermediate and high) according to their Rassi score. A single-lead ECG was recorded for a period of 10–20 min, RR series were generated and 31 HRV indices were calculated. The HRV was compared among the three risk groups and regarding the associated digestive involvement. Four machine learning models were created to predict the risk class of patients. Results Phase entropy is decreased and the percentage of inflection points is increased in patients from the high-, compared to the low-risk group. Fourteen patients had the mixed form, showing decreased triangular interpolation of the RR histogram and absolute power at the low-frequency band. The best predictive risk model was obtained by the support vector machine algorithm (overall F1-score of 0.61). Conclusions The mixed form of Chagas' disease showed a decrease in the slow HRV components. The worst prognosis in CCC is associated with increased heart rate fragmentation. The combination of HRV indices enhanced the accuracy of risk stratification. In patients with the mixed form of Chagas disease, a higher degree of sympathetic autonomic denervation may be associated with parasympathetic impairment.

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Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants Compared to Full-Length Long Signals

Cited by 10 publications

References 27 publications

Quantifying Heart Rate Variability Using Multiscale Fuzzy Dispersion Entropy

Quantifying Heart Rate Variability Using Multiscale Fuzzy Dispersion Entropy

Robust Spike-Based Continual Meta-Learning Improved by Restricted Minimum Error Entropy Criterion

Heart rate variability as a biomarker in patients with Chronic Chagas Cardiomyopathy with or without concomitant digestive involvement and its relationship with the Rassi score

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