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Grivel E, Berthelot B, Colin G, Legrand P, Ibanez V. Benefits of Zero-Phase or Linear Phase Filters to Design Multiscale Entropy: Theory and Application. ENTROPY (BASEL, SWITZERLAND) 2024; 26:332. [PMID: 38667886 PMCID: PMC11048990 DOI: 10.3390/e26040332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 03/16/2024] [Accepted: 04/06/2024] [Indexed: 04/28/2024]
Abstract
In various applications, multiscale entropy (MSE) is often used as a feature to characterize the complexity of the signals in order to classify them. It consists of estimating the sample entropies (SEs) of the signal under study and its coarse-grained (CG) versions, where the CG process amounts to (1) filtering the signal with an average filter whose order is the scale and (2) decimating the filter output by a factor equal to the scale. In this paper, we propose to derive a new variant of the MSE. Its novelty stands in the way to get the sequences at different scales by avoiding distortions during the decimation step. To this end, a linear-phase or null-phase low-pass filter whose cutoff frequency is well suited to the scale is used. Interpretations on how the MSE behaves and illustrations with a sum of sinusoids, as well as white and pink noises, are given. Then, an application to detect attentional tunneling is presented. It shows the benefit of the new approach in terms of p value when one aims at differentiating the set of MSEs obtained in the attentional tunneling state from the set of MSEs obtained in the nominal state. It should be noted that CG versions can be replaced not only for the MSE but also for other variants.
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Affiliation(s)
- Eric Grivel
- IMS Laboratory, Bordeaux INP, Bordeaux University, UMR CNRS 5218, 33400 Talence, France
| | - Bastien Berthelot
- Thales AVS France, Campus Merignac, 75-77 Av. Marcel Dassault, 33700 Mérignac, France; (B.B.); (V.I.)
| | - Gaetan Colin
- ENSEIRB-MATMECA, Bordeaux INP, 33400 Talence, France
| | - Pierrick Legrand
- IMB Laboratory, Bordeaux University, UMR CNRS 5251, ASTRAL Team, INRIA, 33400 Talence, France;
| | - Vincent Ibanez
- Thales AVS France, Campus Merignac, 75-77 Av. Marcel Dassault, 33700 Mérignac, France; (B.B.); (V.I.)
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2
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Nawaz S, Saleem M, Kusmartsev FV, Anjum DH. Major Role of Multiscale Entropy Evolution in Complex Systems and Data Science. ENTROPY (BASEL, SWITZERLAND) 2024; 26:330. [PMID: 38667884 PMCID: PMC11048943 DOI: 10.3390/e26040330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 04/07/2024] [Accepted: 04/10/2024] [Indexed: 04/28/2024]
Abstract
Complex systems are prevalent in various disciplines encompassing the natural and social sciences, such as physics, biology, economics, and sociology. Leveraging data science techniques, particularly those rooted in artificial intelligence and machine learning, offers a promising avenue for comprehending the intricacies of complex systems without necessitating detailed knowledge of underlying dynamics. In this paper, we demonstrate that multiscale entropy (MSE) is pivotal in describing the steady state of complex systems. Introducing the multiscale entropy dynamics (MED) methodology, we provide a framework for dissecting system dynamics and uncovering the driving forces behind their evolution. Our investigation reveals that the MED methodology facilitates the expression of complex system dynamics through a Generalized Nonlinear Schrödinger Equation (GNSE) that thus demonstrates its potential applicability across diverse complex systems. By elucidating the entropic underpinnings of complexity, our study paves the way for a deeper understanding of dynamic phenomena. It offers insights into the behavior of complex systems across various domains.
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Affiliation(s)
- Shahid Nawaz
- Department of Physics, Loughborough University, Loughborough LE11 3TU, UK
| | - Muhammad Saleem
- Department of Physics, Bellarmine University, 2001 Newburg Road, Louisville, KY 40205, USA
| | - Fedor V. Kusmartsev
- Department of Physics, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
| | - Dalaver H. Anjum
- Department of Physics, Khalifa University, Abu Dhabi P.O. Box 127788, United Arab Emirates
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3
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Cataldo A, Criscuolo S, De Benedetto E, Masciullo A, Pesola M, Schiavoni R. A Novel Metric for Alzheimer's Disease Detection Based on Brain Complexity Analysis via Multiscale Fuzzy Entropy. Bioengineering (Basel) 2024; 11:324. [PMID: 38671746 PMCID: PMC11048692 DOI: 10.3390/bioengineering11040324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 03/22/2024] [Accepted: 03/26/2024] [Indexed: 04/28/2024] Open
Abstract
Alzheimer's disease (AD) is a neurodegenerative brain disorder that affects cognitive functioning and memory. Current diagnostic tools, including neuroimaging techniques and cognitive questionnaires, present limitations such as invasiveness, high costs, and subjectivity. In recent years, interest has grown in using electroencephalography (EEG) for AD detection due to its non-invasiveness, low cost, and high temporal resolution. In this regard, this work introduces a novel metric for AD detection by using multiscale fuzzy entropy (MFE) to assess brain complexity, offering clinicians an objective, cost-effective diagnostic tool to aid early intervention and patient care. To this purpose, brain entropy patterns in different frequency bands for 35 healthy subjects (HS) and 35 AD patients were investigated. Then, based on the resulting MFE values, a specific detection algorithm, able to assess brain complexity abnormalities that are typical of AD, was developed and further validated on 24 EEG test recordings. This MFE-based method achieved an accuracy of 83% in differentiating between HS and AD, with a diagnostic odds ratio of 25, and a Matthews correlation coefficient of 0.67, indicating its viability for AD diagnosis. Furthermore, the algorithm showed potential for identifying anomalies in brain complexity when tested on a subject with mild cognitive impairment (MCI), warranting further investigation in future research.
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Affiliation(s)
- Andrea Cataldo
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.M.); (R.S.)
| | - Sabatina Criscuolo
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy; (S.C.); (E.D.B.); (M.P.)
| | - Egidio De Benedetto
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy; (S.C.); (E.D.B.); (M.P.)
| | - Antonio Masciullo
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.M.); (R.S.)
| | - Marisa Pesola
- Department of Electrical Engineering and Information Technology (DIETI), University of Naples Federico II, 80125 Naples, Italy; (S.C.); (E.D.B.); (M.P.)
| | - Raissa Schiavoni
- Department of Engineering for Innovation, University of Salento, 73100 Lecce, Italy; (A.M.); (R.S.)
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Pan W, Li H, Zhou X, Jiao J, Zhu C, Zhang Q. Research on Pig Sound Recognition Based on Deep Neural Network and Hidden Markov Models. SENSORS (BASEL, SWITZERLAND) 2024; 24:1269. [PMID: 38400427 PMCID: PMC10891870 DOI: 10.3390/s24041269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 02/04/2024] [Accepted: 02/15/2024] [Indexed: 02/25/2024]
Abstract
In order to solve the problem of low recognition accuracy of traditional pig sound recognition methods, deep neural network (DNN) and Hidden Markov Model (HMM) theory were used as the basis of pig sound signal recognition in this study. In this study, the sounds made by 10 landrace pigs during eating, estrus, howling, humming and panting were collected and preprocessed by Kalman filtering and an improved endpoint detection algorithm based on empirical mode decomposition-Teiger energy operator (EMD-TEO) cepstral distance. The extracted 39-dimensional mel-frequency cepstral coefficients (MFCCs) were then used as a dataset for network learning and recognition to build a DNN- and HMM-based sound recognition model for pig states. The results show that in the pig sound dataset, the recognition accuracy of DNN-HMM reaches 83%, which is 22% and 17% higher than that of the baseline models HMM and GMM-HMM, and possesses a better recognition effect. In a sub-dataset of the publicly available dataset AudioSet, DNN-HMM achieves a recognition accuracy of 79%, which is 8% and 4% higher than the classical models SVM and ResNet18, respectively, with better robustness.
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Affiliation(s)
- Weihao Pan
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
| | - Hualong Li
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China
| | - Xiaobo Zhou
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
| | - Jun Jiao
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
| | - Cheng Zhu
- School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
| | - Qiang Zhang
- Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
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5
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Huang PH, Hsiao TC. Use of Intrinsic Entropy to Assess the Instantaneous Complexity of Thoracoabdominal Movement Patterns to Indicate the Effect of the Iso-Volume Maneuver Trial on the Performance of the Step Test. ENTROPY (BASEL, SWITZERLAND) 2023; 26:27. [PMID: 38248153 PMCID: PMC10814788 DOI: 10.3390/e26010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/21/2023] [Accepted: 12/23/2023] [Indexed: 01/23/2024]
Abstract
The recent surge in interest surrounds the analysis of physiological signals with a non-linear dynamic approach. The measurement of entropy serves as a renowned method for indicating the complexity of a signal. However, there is a dearth of research concerning the non-linear dynamic analysis of respiratory signals. Therefore, this study employs a novel method known as intrinsic entropy (IE) to assess the short-term dynamic changes in thoracoabdominal movement patterns, as measured by respiratory inductance plethysmography (RIP), during various states such as resting, step test, recovery, and iso-volume maneuver (IVM) trials. The findings reveal a decrease in IE of thoracic wall movement (TWM) and an increase in IE of abdominal wall movement (AWM) following the IVM trial. This suggests that AWM may dominate the breathing exercise after the IVM trial. Moreover, due to the high temporal resolution of IE, it proves to be a suitable measure for assessing the complexity of thoracoabdominal movement patterns under non-stationary states such as the step test and recovery. The results also demonstrate that the instantaneous complexity of TWM and AWM can effectively capture instantaneous changes during non-stationary states, which may prove valuable in understanding the respiratory mechanism for healthcare purposes in daily life.
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Affiliation(s)
- Po-Hsun Huang
- Institute of Computer Science and Engineering, College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
| | - Tzu-Chien Hsiao
- Institute of Computer Science and Engineering, College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan;
- Department of Computer Science, College of Computer Science, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
- Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu 30010, Taiwan
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6
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Lakhal S, Darmon A, Mastromatteo I, Marsili M, Benzaquen M. Multiscale relevance of natural images. Sci Rep 2023; 13:14879. [PMID: 37689770 PMCID: PMC10492821 DOI: 10.1038/s41598-023-41714-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Accepted: 08/30/2023] [Indexed: 09/11/2023] Open
Abstract
We use an agnostic information-theoretic approach to investigate the statistical properties of natural images. We introduce the Multiscale Relevance (MSR) measure to assess the robustness of images to compression at all scales. Starting in a controlled environment, we characterize the MSR of synthetic random textures as function of image roughness [Formula: see text] and other relevant parameters. We then extend the analysis to natural images and find striking similarities with critical ([Formula: see text]) random textures. We show that the MSR is more robust and informative of image content than classical methods such as power spectrum analysis. Finally, we confront the MSR to classical measures for the calibration of common procedures such as color mapping and denoising. Overall, the MSR approach appears to be a good candidate for advanced image analysis and image processing, while providing a good level of physical interpretability.
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Affiliation(s)
- Samy Lakhal
- Chair of Econophysics and Complex Systems, Ecole Polytechnique, 91128, Palaiseau Cedex, France
- LadHyX, UMR CNRS 7646, Ecole Polytechnique, 91128, Palaiseau Cedex, France
- Institut Jean Le Rond d'Alembert, UMR CNRS 7190, Sorbonne Université, 75005, Paris, France
| | | | - Iacopo Mastromatteo
- Chair of Econophysics and Complex Systems, Ecole Polytechnique, 91128, Palaiseau Cedex, France
- Capital Fund Management, 23 Rue de l'Université, 75007, Paris, France
| | - Matteo Marsili
- Quantitative Life Sciences Section, The Abdus Salam International Centre for Theoretical Physics, 34151, Trieste, Italy
| | - Michael Benzaquen
- Chair of Econophysics and Complex Systems, Ecole Polytechnique, 91128, Palaiseau Cedex, France.
- LadHyX, UMR CNRS 7646, Ecole Polytechnique, 91128, Palaiseau Cedex, France.
- Capital Fund Management, 23 Rue de l'Université, 75007, Paris, France.
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7
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Hutcheon EA, Vakorin VA, Nunes A, Ribary U, Ferguson S, Claydon VE, Doesburg SM. Associations between spontaneous electroencephalogram oscillations and oxygen saturation across normobaric and hypobaric hypoxia. Hum Brain Mapp 2023; 44:2345-2364. [PMID: 36715216 PMCID: PMC10028628 DOI: 10.1002/hbm.26214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 12/12/2022] [Accepted: 01/09/2023] [Indexed: 01/31/2023] Open
Abstract
High-altitude indoctrination (HAI) trains individuals to recognize symptoms of hypoxia by simulating high-altitude conditions using normobaric (NH) or hypobaric (HH) hypoxia. Previous studies suggest that despite equivalent inspired oxygen levels, physiological differences could exist between these conditions. In particular, differences in neurophysiological responses to these conditions are not clear. Our study aimed to investigate correlations between oxygen saturation (SpO2 ) and neural responses in NH and HH. We recorded 5-min of resting-state eyes-open electroencephalogram (EEG) and SpO2 during control, NH, and HH conditions from 13 participants. We applied a multivariate framework to characterize correlations between SpO2 and EEG measures (spectral power and multiscale entropy [MSE]), within each participant and at the group level. Participants were desaturating during the first 150 s of NH versus steadily desaturated in HH. We considered the entire time interval, first and second half intervals, separately. All the conditions were characterized by statistically significant participant-specific patterns of EEG-SpO2 correlations. However, at the group level, the desaturation period expressed a robust pattern of these correlations across frequencies and brain locations. Specifically, the first 150 s of NH during desaturation differed significantly from the other conditions with negative absolute alpha power-SpO2 correlations and positive MSE-SpO2 correlations. Once steadily desaturated, NH and HH had no significant differences in EEG-SpO2 correlations. Our findings indicate that the desaturating phase of hypoxia is a critical period in HAI courses, which would require developing strategies for mitigating the hypoxic stimulus in a real-world situation.
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Affiliation(s)
- Evan A Hutcheon
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Vasily A Vakorin
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Adonay Nunes
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
- Harvard Medical School, Harvard University, Boston, Massachusetts, USA
| | - Urs Ribary
- Department of Psychology, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Sherri Ferguson
- Environmental Physiology and Medicine Unit, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Victoria E Claydon
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
| | - Sam M Doesburg
- Department of Biomedical Physiology and Kinesiology, Simon Fraser University, Burnaby, British Columbia, Canada
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8
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Jabloun M, Ravier P, Buttelli O. On the Genuine Relevance of the Data-Driven Signal Decomposition-Based Multiscale Permutation Entropy. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1343. [PMID: 37420363 DOI: 10.3390/e24101343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/12/2022] [Accepted: 09/14/2022] [Indexed: 07/09/2023]
Abstract
Ordinal pattern-based approaches have great potential to capture intrinsic structures of dynamical systems, and therefore, they continue to be developed in various research fields. Among these, the permutation entropy (PE), defined as the Shannon entropy of ordinal probabilities, is an attractive time series complexity measure. Several multiscale variants (MPE) have been proposed in order to bring out hidden structures at different time scales. Multiscaling is achieved by combining linear or nonlinear preprocessing with PE calculation. However, the impact of such a preprocessing on the PE values is not fully characterized. In a previous study, we have theoretically decoupled the contribution of specific signal models to the PE values from that induced by the inner correlations of linear preprocessing filters. A variety of linear filters such as the autoregressive moving average (ARMA), Butterworth, and Chebyshev were tested. The current work is an extension to nonlinear preprocessing and especially to data-driven signal decomposition-based MPE. The empirical mode decomposition, variational mode decomposition, singular spectrum analysis-based decomposition and empirical wavelet transform are considered. We identify possible pitfalls in the interpretation of PE values induced by these nonlinear preprocessing, and hence, we contribute to improving the PE interpretation. The simulated dataset of representative processes such as white Gaussian noise, fractional Gaussian processes, ARMA models and synthetic sEMG signals as well as real-life sEMG signals are tested.
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Affiliation(s)
- Meryem Jabloun
- Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique, Énergétique (PRISME), University of Orleans, 45100 Orleans, France
| | - Philippe Ravier
- Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique, Énergétique (PRISME), University of Orleans, 45100 Orleans, France
| | - Olivier Buttelli
- Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique, Énergétique (PRISME), University of Orleans, 45100 Orleans, France
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9
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Lebreton C, Kbidi F, Graillet A, Jegado T, Alicalapa F, Benne M, Damour C. PV System Failures Diagnosis Based on Multiscale Dispersion Entropy. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1311. [PMID: 36141197 PMCID: PMC9497466 DOI: 10.3390/e24091311] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 08/12/2022] [Accepted: 08/17/2022] [Indexed: 06/16/2023]
Abstract
Photovoltaic (PV) system diagnosis is a growing research domain likewise solar energy's ongoing significant expansion. Indeed, efficient Fault Detection and Diagnosis (FDD) tools are crucial to guarantee reliability, avoid premature aging and improve the profitability of PV plants. In this paper, an on-line diagnosis method using the PV plant electrical output is presented. This entirely signal-based method combines variational mode decomposition (VMD) and multiscale dispersion entropy (MDE) for the purpose of detecting and isolating faults in a real grid-connected PV plant. The present method seeks a low-cost design, an ease of implementation and a low computation cost. Taking into account the innovation of applying these techniques to PV FDD, the VMD and MDE procedures as well as parameters identification are carefully detailed. The proposed FFD approach performance is assessed on a real rooftop PV plant with experimentally induced faults, and the first results reveal the MDE approach has good suitability for PV plants diagnosis.
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10
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Quantification of Small-Scale Heterogeneity at the Core–Mantle Boundary Using Sample Entropy of SKS and SPdKS Synthetic Waveforms. MINERALS 2022. [DOI: 10.3390/min12070813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Qualitative and quantitative analysis of seismic waveforms sensitive to the core–mantle boundary (CMB) region reveal the presence of ultralow-velocity zones (ULVZs) that have a strong decrease in compressional (P) and shear (S) wave velocity, and an increase in density within thin structures. However, understanding their physical origin and relation to the other large-scale structures in the lowermost mantle are limited due to an incomplete mapping of ULVZs at the CMB. The SKS and SPdKS seismic waveforms is routinely used to infer ULVZ presence, but has thus far only been used in a limited epicentral distance range. As the SKS/SPdKS wavefield interacts with a ULVZ it generates additional seismic arrivals, thus increasing the complexity of the recorded wavefield. Here, we explore utilization of the multi-scale sample entropy method to search for ULVZ structures. We investigate the feasibility of this approach through analysis of synthetic seismograms computed for PREM, 1-, 2.5-, and 3-D ULVZs as well as heterogeneous structures with a strong increase in velocity in the lowermost mantle in 1- and 2.5-D. We find that the sample entropy technique may be useful across a wide range of epicentral distances from 100° to 130°. Such an analysis, when applied to real waveforms, could provide coverage of roughly 85% by surface area of the CMB.
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11
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John AT, Barthel A, Wind J, Rizzi N, Schöllhorn WI. Acute Effects of Various Movement Noise in Differential Learning of Rope Skipping on Brain and Heart Recovery Analyzed by Means of Multiscale Fuzzy Measure Entropy. Front Behav Neurosci 2022; 16:816334. [PMID: 35283739 PMCID: PMC8914377 DOI: 10.3389/fnbeh.2022.816334] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 01/18/2022] [Indexed: 11/13/2022] Open
Abstract
In search of more detailed explanations for body-mind interactions in physical activity, neural and physiological effects, especially regarding more strenuous sports activities, increasingly attract interest. Little is known about the underlying manifold (neuro-)physiological impacts induced by different motor learning approaches. The various influences on brain or cardiac function are usually studied separately and modeled linearly. Limitations of these models have recently led to a rapidly growing application of nonlinear models. This study aimed to investigate the acute effects of various sequences of rope skipping on irregularity of the electrocardiography (ECG) and electroencephalography (EEG) signals as well as their interaction and whether these depend on different levels of active movement noise, within the framework of differential learning theory. Thirty-two males were randomly and equally distributed to one of four rope skipping conditions with similar cardiovascular but varying coordinative demand. ECG and EEG were measured simultaneously at rest before and immediately after rope skipping for 25 mins. Signal irregularity of ECG and EEG was calculated via the multiscale fuzzy measure entropy (MSFME). Statistically significant ECG and EEG brain area specific changes in MSFME were found with different pace of occurrence depending on the level of active movement noise of the particular rope skipping condition. Interaction analysis of ECG and EEG MSFME specifically revealed an involvement of the frontal, central, and parietal lobe in the interplay with the heart. In addition, the number of interaction effects indicated an inverted U-shaped trend presenting the interaction level of ECG and EEG MSFME dependent on the level of active movement noise. In summary, conducting rope skipping with varying degrees of movement variation appears to affect the irregularity of cardiac and brain signals and their interaction during the recovery phase differently. These findings provide enough incentives to foster further constructive nonlinear research in exercise-recovery relationship and to reconsider the philosophy of classical endurance training.
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Affiliation(s)
- Alexander Thomas John
- Department of Training and Movement Science, Institute of Sport Science, Johannes Gutenberg-University, Mainz, Germany
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12
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Bosl WJ, Loddenkemper T, Vieluf S. Coarse-graining and the Haar wavelet transform for multiscale analysis. Bioelectron Med 2022; 8:3. [PMID: 35105373 PMCID: PMC8809023 DOI: 10.1186/s42234-022-00085-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 01/18/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Multiscale entropy (MSE) has become increasingly common as a quantitative tool for analysis of physiological signals. The MSE computation involves first decomposing a signal into multiple sub-signal 'scales' using a coarse-graining algorithm. METHODS The coarse-graining algorithm averages adjacent values in a time series to produce a coarser scale time series. The Haar wavelet transform convolutes a time series with a scaled square wave function to produce an approximation which is equivalent to averaging points. RESULTS Coarse-graining is mathematically identical to the Haar wavelet transform approximations. Thus, multiscale entropy is entropy computed on sub-signals derived from approximations of the Haar wavelet transform. By describing coarse-graining algorithms properly as Haar wavelet transforms, the meaning of 'scales' as wavelet approximations becomes transparent. The computed value of entropy is different with different wavelet basis functions, suggesting further research is needed to determine optimal methods for computing multiscale entropy. CONCLUSION Coarse-graining is mathematically identical to Haar wavelet approximations at power-of-two scales. Referring to coarse-graining as a Haar wavelet transform motivates research into the optimal approach to signal decomposition for entropy analysis.
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Affiliation(s)
- William J Bosl
- University of San Francisco, 2130 Fulton Street, San Francisco, CA, 94117, USA.
- Department of Pediatrics, Harvard Medical School, Boston, USA.
- Computational Health Informatics Program, Boston Children's Hospital, Boston, USA.
| | - Tobias Loddenkemper
- Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Solveig Vieluf
- Department of Neurology, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Institute of Sports Medicine, Paderborn University, Warburger Str. 100, 33098, Paderborn, Germany
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13
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Jiang R, Wu W, Yu Y, Ma F. An Intelligent Control Model of Credit Line Computing in Intelligence Health-Care Systems. Front Public Health 2021; 9:718594. [PMID: 34568259 PMCID: PMC8462519 DOI: 10.3389/fpubh.2021.718594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Accepted: 07/31/2021] [Indexed: 11/13/2022] Open
Abstract
Technologies such as machine learning and artificial intelligence have brought about a tremendous change to biomedical computing and intelligence health care. As a principal component of the intelligence healthcare system, the hospital information system (HIS) has provided great convenience to hospitals and patients, but incidents of leaking private information of patients through HIS occasionally occur at times. Therefore, it is necessary to properly control excessive access behavior. To reduce the risk of patient privacy leakage when medical data are accessed, this article proposes a dynamic permission intelligent access control model that introduces credit line calculation. According to the target given by the doctor in HIS and the actual access record, the International Classification of Diseases (ICD)-10 code is used to describe the degree of correlation, and the rationality of the access is formally described by a mathematical formula. The concept of intelligence healthcare credit lines is redefined with relevance and time Windows. The access control policy matches the corresponding credit limit and credit interval according to the authorization rules to achieve the purpose of intelligent control. Finally, with the actual data provided by a Grade-III Level-A hospital in Kunming, the program code is written through machine learning and biomedical computing-related technologies to complete the experimental test. The experiment proves that the intelligent access control model based on credit computing proposed in this study can play a role in protecting the privacy of patients to a certain extent.
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Affiliation(s)
- Rong Jiang
- Institute of Intelligence Applications, Yunnan University of Finance and Economics, Kunming, China.,Key Laboratory of Service Computing and Safety Management of Yunnan Provincial Universities, Kunming, China.,Kunming Key Laboratory of Information Economy & Information Management, Kunming, China
| | - Wenxuan Wu
- Institute of Intelligence Applications, Yunnan University of Finance and Economics, Kunming, China.,Key Laboratory of Service Computing and Safety Management of Yunnan Provincial Universities, Kunming, China.,Kunming Key Laboratory of Information Economy & Information Management, Kunming, China.,School of Information, Yunnan University of Finance and Economics, Kunming, China
| | - Yimin Yu
- Institute of Intelligence Applications, Yunnan University of Finance and Economics, Kunming, China.,Key Laboratory of Service Computing and Safety Management of Yunnan Provincial Universities, Kunming, China.,Kunming Key Laboratory of Information Economy & Information Management, Kunming, China.,School of Information, Yunnan University of Finance and Economics, Kunming, China
| | - Feng Ma
- School of Information, Yunnan University of Finance and Economics, Kunming, China
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14
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Yu WY, Low I, Chen C, Fuh JL, Chen LF. Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer's Disease and Mild Cognitive Impairment. ENTROPY (BASEL, SWITZERLAND) 2021; 23:427. [PMID: 33916588 PMCID: PMC8066899 DOI: 10.3390/e23040427] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/30/2021] [Accepted: 03/31/2021] [Indexed: 12/22/2022]
Abstract
Individuals with mild cognitive impairment (MCI) are at high risk of developing Alzheimer's disease (AD). Repetitive photic stimulation (PS) is commonly used in routine electroencephalogram (EEG) examinations for rapid assessment of perceptual functioning. This study aimed to evaluate neural oscillatory responses and nonlinear brain dynamics under the effects of PS in patients with mild AD, moderate AD, severe AD, and MCI, as well as healthy elderly controls (HC). EEG power ratios during PS were estimated as an index of oscillatory responses. Multiscale sample entropy (MSE) was estimated as an index of brain dynamics before, during, and after PS. During PS, EEG harmonic responses were lower and MSE values were higher in the AD subgroups than in HC and MCI groups. PS-induced changes in EEG complexity were less pronounced in the AD subgroups than in HC and MCI groups. Brain dynamics revealed a "transitional change" between MCI and Mild AD. Our findings suggest a deficiency in brain adaptability in AD patients, which hinders their ability to adapt to repetitive perceptual stimulation. This study highlights the importance of combining spectral and nonlinear dynamical analysis when seeking to unravel perceptual functioning and brain adaptability in the various stages of neurodegenerative diseases.
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Grants
- AS-BD-108-2 Academia Sinica, Taiwan
- MOST 109-2314-B-010-027, 107-2221-E-010-013, 109-2811-E-010-503, 108-2321-B-075-001, 109-2314-B-075-052-MY2 Ministry of Science and Technology, Taiwan
- VGHUST 110-G1-5-1, 110-G1-5-2, 109-V1-5-1, 109-V1-5-2 Veterans General Hospitals-University System of Taiwan Joint Research Program
- V110C-057 Taipei Veterans General Hospital
- Brain Research Center, National Yang Ming Chiao Tung University from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project Taiwan Ministry of Education
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Affiliation(s)
- Wei-Yang Yu
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (W.-Y.Y.); (I.L.)
| | - Intan Low
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (W.-Y.Y.); (I.L.)
- Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan
| | - Chien Chen
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Jong-Ling Fuh
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Li-Fen Chen
- Institute of Brain Science, National Yang Ming Chiao Tung University, Taipei 112, Taiwan; (W.-Y.Y.); (I.L.)
- Integrated Brain Research Unit, Department of Medical Research, Taipei Veterans General Hospital, Taipei 112, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
- Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
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