Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants

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

doi:10.21203/rs.3.rs-361154/v1

Cited by 2 publications

(1 citation statement)

References 20 publications

(37 reference statements)

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“…Moreover, a slight difference between the results obtained with FE and SE could indicate a higher accuracy for FE. Results showed in Table 2 and Table 4 highlighted that the MFE index was able to catch the differences on more scales than MSE, showing more consistency and intrinsic robustness against noise in the time series [ 51 ]. The MSE and MFE entropies showed promising results, thus suggesting that higher scale levels should be exploited using variants of MSE, such as Modified multiscale entropy or others [ 35 , 36 ].…”

Section: Discussionmentioning

confidence: 99%

Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures

et al. 2021

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The complex physiological dynamics of neonatal seizures make their detection challenging. A timely diagnosis and treatment, especially in intensive care units, are essential for a better prognosis and the mitigation of possible adverse effects on the newborn’s neurodevelopment. In the literature, several electroencephalographic (EEG) studies have been proposed for a parametric characterization of seizures or their detection by artificial intelligence techniques. At the same time, other sources than EEG, such as electrocardiography, have been investigated to evaluate the possible impact of neonatal seizures on the cardio-regulatory system. Heart rate variability (HRV) analysis is attracting great interest as a valuable tool in newborns applications, especially where EEG technologies are not easily available. This study investigated whether multiscale HRV entropy indexes could detect abnormal heart rate dynamics in newborns with seizures, especially during ictal events. Furthermore, entropy measures were analyzed to discriminate between newborns with seizures and seizure-free ones. A cohort of 52 patients (33 with seizures) from the Helsinki University Hospital public dataset has been evaluated. Multiscale sample and fuzzy entropy showed significant differences between the two groups (p-value < 0.05, Bonferroni multiple-comparison post hoc correction). Moreover, interictal activity showed significant differences between seizure and seizure-free patients (Mann-Whitney Test: p-value < 0.05). Therefore, our findings suggest that HRV multiscale entropy analysis could be a valuable pre-screening tool for the timely detection of seizure events in newborns.

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Section: Discussionmentioning

confidence: 99%

Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures

et al. 2021

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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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Multiscale Entropy Analysis of Short Signals: The Robustness of Fuzzy Entropy-Based Variants

Cited by 2 publications

References 20 publications

Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures

Multiscale Entropy Analysis of Heart Rate Variability in Neonatal Patients with and without Seizures

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

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