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https://www.readbyqxmd.com/read/30425614/measuring-brain-complexity-during-neural-motor-resonance
#1
Brandon M Hager, Albert C Yang, Jennifer N Gutsell
Background: EEG mu-desynchronization is an index of motor resonance (MR) and is used to study social interaction deficiencies, but finding differences in mu-desynchronization does not reveal how nonlinear brain dynamics are affected during MR. The current study explores how nonlinear brain dynamics change during MR. We hypothesized that the complexity of the mu frequency band (8-13 Hz) changes during MR, and that this change would be frequency specific. Additionally, we sought to determine whether complexity at baseline and changes in complexity during action observation would predict MR and changes in network dynamics...
2018: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/30417551/shared-neural-and-cognitive-mechanisms-in-action-and-language-the-multiscale-information-transfer-framework
#2
REVIEW
Alice Blumenthal-Dramé, Evie Malaia
This review compares how humans process action and language sequences produced by other humans. On the one hand, we identify commonalities between action and language processing in terms of cognitive mechanisms (e.g., perceptual segmentation, predictive processing, integration across multiple temporal scales), neural resources (e.g., the left inferior frontal cortex), and processing algorithms (e.g., comprehension based on changes in signal entropy). On the other hand, drawing on sign language with its particularly strong motor component, we also highlight what differentiates (both oral and signed) linguistic communication from nonlinguistic action sequences...
November 12, 2018: Wiley Interdisciplinary Reviews. Cognitive Science
https://www.readbyqxmd.com/read/30415711/application-of-multiscale-poincar%C3%A3-short-time-computation-versus-multiscale-entropy-in-analyzing-fingertip-photoplethysmogram-amplitudes-to-differentiate-diabetic-from-non-diabetic-subjects
#3
Bagus Haryadi, Juin J Liou, Hai-Cheng Wei, Ming-Xia Xiao, Hsien-Tsai Wu, Cheuk-Kwan Sun
BACKGROUND AND OBJECTIVES: Multiscale Poincaré (MSP) plots have recently been introduced to facilitate the visualization of time series of physiological signals. This study aimed at investigating the feasibility of MSP application in distinguishing subjects with and without diabetes. METHODS: Using photoplethysmogram (PPG) waveform amplitudes acquired from unilateral fingertip of non-diabetic (n = 34) and diabetic (n = 30) subjects, MSP indices (MSPI) of the two groups were compared using 1000, 500, 250, 100 data points...
November 2018: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/30399022/comparison-of-invasive-and-noninvasive-blood-pressure-measurements-for-assessing-signal-complexity-and-surgical-risk-in-cardiac-surgical-patients
#4
Lauren E Gibson, Teresa S Henriques, Madalena D Costa, Roger B Davis, Murray A Mittleman, Pooja Mathur, Balachundhar Subramaniam
BACKGROUND: Continuous arterial blood pressure (ABP) is typically recorded by placement of an intraarterial catheter. Recently, noninvasive ABP monitors have been shown to be comparable in accuracy to invasive measurements. In a previous study, we showed that the fluctuations in beat-to-beat ABP measurements were not random variations but had a complex dynamical structure, and that ABP dynamical complexity was inversely associated with surgical risk estimated using the Society of Thoracic Surgeons (STS) index...
November 1, 2018: Anesthesia and Analgesia
https://www.readbyqxmd.com/read/30375254/multiscale-entropy-analysis-for-recognition-of-visually-elicited-negative-stress-from-eeg-recordings
#5
Arturo Martínez-Rodrigo, Beatriz García-Martínez, Raúl Alcaraz, Pascual González, Antonio Fernández-Caballero
Automatic identification of negative stress is an unresolved challenge that has received great attention in the last few years. Many studies have analyzed electroencephalographic (EEG) recordings to gain new insights about how the brain reacts to both short- and long-term stressful stimuli. Although most of them have only considered linear methods, the heterogeneity and complexity of the brain has recently motivated an increasing use of nonlinear metrics. Nonetheless, brain dynamics reflected in EEG recordings often exhibit a multiscale nature and no study dealing with this aspect has been developed yet...
August 24, 2018: International Journal of Neural Systems
https://www.readbyqxmd.com/read/30368171/multidimensional-and-fuzzy-sample-entropy-sampen-mf-for-quantifying-h-e-histological-images-of-colorectal-cancer
#6
Luiz Fernando Segato Dos Santos, Leandro Alves Neves, Guilherme Botazzo Rozendo, Matheus Gonçalves Ribeiro, Marcelo Zanchetta do Nascimento, Thaína Aparecida Azevedo Tosta
In this study, we propose to use a method based on the combination of sample entropy with multiscale and multidimensional approaches, along with a fuzzy function. The model was applied to quantify and classify H&E histological images of colorectal cancer. The multiscale approach was defined by analysing windows of different sizes and variations in tolerance for determining pattern similarity. The multidimensional strategy was performed by considering each pixel in the colour image as an n-dimensional vector, which was analysed from the Minkowski distance...
October 18, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/30353094/the-association-between-heart-rhythm-complexity-and-the-severity-of-abdominal-aorta-calcification-in-peritoneal-dialysis-patients
#7
Cheng-Hsuan Tsai, Chen Lin, Yi-Heng Ho, Men-Tzung Lo, Li-Yu Daisy Liu, Chih-Ting Lin, Jenq-Wen Huang, Chung-Kang Peng, Yen-Hung Lin
Abdominal aorta calcification (AAC) has been associated with clinical outcomes in peritoneal dialysis (PD) patients. Heart rhythm complexity analysis has been shown to be a promising tool to predict outcomes in patients with cardiovascular disease. In this study, we aimed to analyze the association between heart rhythm complexity and AAC in PD patients. We prospectively analyzed 133 PD patients. Heart rhythm complexity including detrended fluctuation analysis and multiscale entropy was performed. In linear analysis, the patients in the higher AAC group (AAC ≥15%) had a significantly lower standard deviation of normal RR intervals, very low frequency, low frequency, high frequency and low/high frequency ratio...
October 23, 2018: Scientific Reports
https://www.readbyqxmd.com/read/30343216/on-the-choice-of-multiscale-entropy-algorithm-for-quantification-of-complexity-in-gait-data
#8
Peter C Raffalt, William Denton, Jennifer M Yentes
The present study aimed at identifying a suitable multiscale entropy (MSE) algorithm for assessment of complexity in a stride-to-stride time interval time series. Five different algorithms were included (the original MSE, refine composite multiscale entropy (RCMSE), multiscale fuzzy entropy, generalized multiscale entropy and intrinsic mode entropy) and applied to twenty iterations of white noise, pink noise, or a sine wave with added white noise. Based on their ability to differentiate the level of complexity in the three different generated signal types, and their sensitivity and parameter consistency, MSE and RCMSE were deemed most appropriate...
October 10, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/30339434/many-body-delocalization-as-a-quantum-avalanche
#9
Thimothée Thiery, François Huveneers, Markus Müller, Wojciech De Roeck
We propose a multiscale diagonalization scheme to study disordered one-dimensional chains, in particular, the transition between many-body localization (MBL) and the ergodic phase, expected to be governed by resonant spots. Our scheme focuses on the dichotomy of MBL versus validity of the eigenstate thermalization hypothesis. We show that a few natural assumptions imply that the system is localized with probability one at criticality. On the ergodic side, delocalization is induced by a quantum avalanche seeded by large ergodic spots, whose size diverges at the transition...
October 5, 2018: Physical Review Letters
https://www.readbyqxmd.com/read/30337850/topological-pattern-recognition-of-severe-alzheimer-s-disease-via-regularized-supervised-learning-of-eeg-complexity
#10
Miaolin Fan, Albert C Yang, Jong-Ling Fuh, Chun-An Chou
Alzheimer's disease (AD) is a progressive brain disorder with gradual memory loss that correlates to cognitive deficits in the elderly population. Recent studies have shown the potentials of machine learning algorithms to identify biomarkers and functional brain activity patterns across various AD stages using electroencephalography (EEG). In this study, we aim to discover the altered spatio-temporal patterns of EEG complexity associated with AD pathology in different severity levels. We employed the multiscale entropy (MSE), a complexity measure of time series signals, as the biomarkers to characterize the nonlinear complexity at multiple temporal scales...
2018: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/30327587/dynamic-complexity-of-spontaneous-bold-activity-in-alzheimer-s-disease-and-mild-cognitive-impairment-using-multiscale-entropy-analysis
#11
Yan Niu, Bin Wang, Mengni Zhou, Jiayue Xue, Habib Shapour, Rui Cao, Xiaohong Cui, Jinglong Wu, Jie Xiang
Alzheimer's disease (AD) is characterized by progressive deterioration of brain function among elderly people. Studies revealed aberrant correlations in spontaneous blood oxygen level-dependent (BOLD) signals in resting-state functional magnetic resonance imaging (rs-fMRI) over a wide range of temporal scales. However, the study of the temporal dynamics of BOLD signals in subjects with AD and mild cognitive impairment (MCI) remains largely unexplored. Multiscale entropy (MSE) analysis is a method for estimating the complexity of finite time series over multiple time scales...
2018: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/30319365/the-relation-between-white-matter-microstructure-and-network-complexity-implications-for-processing-efficiency
#12
Ian M McDonough, Jonathan T Siegel
Brain structure has been proposed to facilitate as well as constrain functional interactions within brain networks. Simulation models suggest that integrity of white matter (WM) microstructure should be positively related to the complexity of BOLD signal - a measure of network interactions. Using 121 young adults from the Human Connectome Project, we empirically tested whether greater WM integrity would be associated with greater complexity of the BOLD signal during rest via multiscale entropy. Multiscale entropy measures the lack of predictability within a given time series across varying time scales, thus being able to estimate fluctuating signal dynamics within brain networks...
2018: Frontiers in Integrative Neuroscience
https://www.readbyqxmd.com/read/30294254/do-complexity-measures-of-frontal-eeg-distinguish-loss-of-consciousness-in-geriatric-patients-under-anesthesia
#13
Sarah L Eagleman, Don A Vaughn, David R Drover, Caitlin M Drover, Mark S Cohen, Nicholas T Ouellette, M Bruce MacIver
While geriatric patients have a high likelihood of requiring anesthesia, they carry an increased risk for adverse cognitive outcomes from its use. Previous work suggests this could be mitigated by better intraoperative monitoring using indexes defined by several processed electroencephalogram (EEG) measures. Unfortunately, inconsistencies between patients and anesthetic agents in current analysis techniques have limited the adoption of EEG as standard of care. In attempts to identify new analyses that discriminate clinically-relevant anesthesia timepoints, we tested 1/ f frequency scaling as well as measures of complexity from nonlinear dynamics...
2018: Frontiers in Neuroscience
https://www.readbyqxmd.com/read/30286411/analysis-of-microvascular-blood-flow-and-oxygenation-discrimination-between-two-haemodynamic-steady-states-using-nonlinear-measures-and-multiscale-analysis
#14
Marjola Thanaj, Andrew J Chipperfield, Geraldine F Clough
OBJECTIVE: This study investigates the feasibility of the use of nonlinear complexity methods as a tool to identify altered microvascular function often associated with pathological conditions. We evaluate the efficacy of multiscale nonlinear complexity methods to account for the multiple time-scales of processes modulating microvascular network perfusion. METHODS: Microvascular blood flux (BF) and oxygenation (OXY: oxyHb, deoxyHb, totalHb and SO2 %) signals were recorded simultaneously at the same site, from the skin of 15 healthy young male volunteers using combined laser Doppler fluximetry (LDF) and white light spectroscopy...
September 26, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/30259594/changes-in-eeg-multiscale-entropy-and-power-law-frequency-scaling-during-the-human-sleep-cycle
#15
Vladimir Miskovic, Kevin J MacDonald, L Jack Rhodes, Kimberly A Cote
We explored changes in multiscale brain signal complexity and power-law scaling exponents of electroencephalogram (EEG) frequency spectra across several distinct global states of consciousness induced in the natural physiological context of the human sleep cycle. We specifically aimed to link EEG complexity to a statistically unified representation of the neural power spectrum. Further, by utilizing surrogate-based tests of nonlinearity we also examined whether any of the sleep stage-dependent changes in entropy were separable from the linear stochastic effects contained in the power spectrum...
September 26, 2018: Human Brain Mapping
https://www.readbyqxmd.com/read/30237767/applications-of-variability-analysis-techniques-for-continuous-glucose-monitoring-derived-time-series-in-diabetic-patients
#16
Klaus-Dieter Kohnert, Peter Heinke, Lutz Vogt, Petra Augstein, Eckhard Salzsieder
Methods from non-linear dynamics have enhanced understanding of functional dysregulation in various diseases but received less attention in diabetes. This retrospective cross-sectional study evaluates and compares relationships between indices of non-linear dynamics and traditional glycemic variability, and their potential application in diabetes control. Continuous glucose monitoring provided data for 177 subjects with type 1 ( n = 22), type 2 diabetes ( n = 143), and 12 non-diabetic subjects. Each time series comprised 576 glucose values...
2018: Frontiers in Physiology
https://www.readbyqxmd.com/read/30222594/multiscale-network-dynamics-between-heart-rate-and-locomotor-activity-are-altered-in-schizophrenia
#17
Erik Reinertsen, Supreeth P Shashikumar, Amit J Shah, Shamim Nemati, Gari D Clifford
OBJECTIVE: Changes in heart rate (HR) and locomotor activity reflect changes in autonomic physiology, behavior, and mood. These systems may involve interrelated neural circuits that are altered in psychiatric illness, yet their interactions are poorly understood. We hypothesized interactions between HR and locomotor activity could be used to discriminate patients with schizophrenia from controls, and would be less able to discriminate non-psychiatric patients from controls. APPROACH: HR and locomotor activity were recorded via wearable patches in 16 patients with schizophrenia and 19 healthy controls...
October 30, 2018: Physiological Measurement
https://www.readbyqxmd.com/read/30180194/multiscale-permutation-r%C3%A3-nyi-entropy-and-its-application-for-eeg-signals
#18
Yinghuang Yin, Kehui Sun, Shaobo He
There is considerable interest in analyzing the complexity of electroencephalography (EEG) signals. However, some traditional complexity measure algorithms only quantify the complexities of signals, but cannot discriminate different signals very well. To analyze the complexity of epileptic EEG signals better, a new multiscale permutation Rényi entropy (MPEr) algorithm is proposed. In this algorithm, the coarse-grained procedure is introduced by using weighting-averaging method, and the weighted factors are determined by analyzing nonlinear signals...
2018: PloS One
https://www.readbyqxmd.com/read/30170769/sleep-stage-classification-using-single-channel-eog
#19
Md Mosheyur Rahman, Mohammed Imamul Hassan Bhuiyan, Ahnaf Rashik Hassan
Sleep stage classification is an important task for the timely diagnosis of sleep disorders and sleep-related studies. In this paper, automatic scoring of sleep stages using Electrooculogram (EOG) is presented. Single channel EOG signals are analyzed in Discrete Wavelet Transform (DWT) domain employing various statistical features such as Spectral Entropy, Moment-based Measures, Refined Composite Multiscale Dispersion Entropy (RCMDE) and Autoregressive (AR) Model Coefficients. The discriminating ability of the features is studied using the One Way Analysis of Variance (ANOVA) and box plots...
August 22, 2018: Computers in Biology and Medicine
https://www.readbyqxmd.com/read/30154695/developmental-trajectory-of-infant-brain-signal-variability-a-longitudinal-pilot-study
#20
Chiaki Hasegawa, Tetsuya Takahashi, Yuko Yoshimura, Sou Nobukawa, Takashi Ikeda, Daisuke N Saito, Hirokazu Kumazaki, Yoshio Minabe, Mitsuru Kikuchi
The infant brain shows rapid neural network development that considerably influences cognitive and behavioral abilities in later life. Reportedly, this neural development process can be indexed by estimating neural signal complexity. However, the precise developmental trajectory of brain signal complexity during infancy remains elusive. This study was conducted to ascertain the trajectory of magnetoencephalography (MEG) signal complexity from 2 months to 3 years of age in five infants using multiscale entropy (MSE), which captures signal complexity at multiple temporal scales...
2018: Frontiers in Neuroscience
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