scholarly journals The Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED): Development and validation for use in pediatric populations

2021 ◽  
Author(s):  
Meghan H. Puglia ◽  
Jacqueline S. Slobin ◽  
Cabell L. Williams
Keyword(s):  
Cognitive Processing ◽  
Developmental Stages ◽  
Multiscale Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Cognitive States ◽  
Eeg Data ◽  
Pipe Line ◽  
Development And Validation ◽  
Entropy Estimates

It is increasingly understood that moment-to-moment brain signal variability - traditionally modeled out of analyses as mere "noise" - serves a valuable function role and captures properties of brain function related to development, cognitive processing, and psychopathology. Multiscale entropy (MSE) - a measure of signal irregularity across temporal scales - is an increasingly popular analytic technique in human neuroscience. MSE provides insight into the time-structure and (non)linearity of fluctuations in neural activity and network dynamics, capturing the brain's moment-to-moment complexity as it operates on multiple time scales. MSE is emerging as a powerful predictor of developmental processes and outcomes. However, differences in EEG preprocessing and MSE computation make it challenging to compare results across studies. Here, we (1) provide an introduction to MSE for developmental researchers, (2) demonstrate the effect of preprocessing procedures on scale-wise entropy estimates, and (3) establish a standardized preprocessing and entropy estimation pipeline that generates scale-wise entropy estimates that are reliable and capable of differentiating developmental stages and cognitive states. This novel pipeline - the Automated Preprocessing Pipe-Line for the Estimation of Scale-wise Entropy from EEG Data (APPLESEED) is fully automated, customizable, and freely available for download from https://github.com/mhpuglia/APPLESEED. The dataset used herein to develop and validate the pipeline is available for download from https://openneuro.org/datasets/ds003710.

scholarly journals Thalamic activity patterns unfolding over multiple time scales predict seizure onset in absence epilepsy

2020 ◽  
Author(s):  
Jordan Michael Sorokin ◽  
Alex Williams ◽  
Surya Ganguli ◽  
John Huguenard
Keyword(s):  
Activity Patterns ◽  
Absence Epilepsy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Neural Firing ◽  
Single Trial ◽  
Seizure Onset ◽  
Cognitive States ◽  
Ictal Activity ◽  
Low Dimensional

AbstractThe brain has a remarkable, yet poorly understood, capacity to perform rapid dynamic switching between different cognitive states. Absence epilepsy, characterized by sudden transitions to and from highly synchronous thalamocortical oscillations, provides a unique window to investigate rapid state switching. Here we explored the transition into seizures in detail using simultaneous extracellular unit recordings from the thalamocortical circuit in the Scn8a mouse, a validated murine model of absence epilepsy. We find that trial-averaged neural firing in the thalamus, but not cortex, was transiently elevated several seconds prior to seizure onset. However, we observed large single-trial variability in pre-ictal dynamics both within and across subjects, suggesting possible heterogeneous transition dynamics into absence seizures. To quantify the single-trial amplitude and temporal variability, we developed a statistical model, which revealed that individual seizures are preceded by low dimensional neural dynamics that vary in amplitude and time across seizures. Interestingly, the single-trial pre-seizure amplitude modulation uncovered by the model showed strong periodicity over trials, suggesting that pre-ictal dynamics may co-modulate with arousal state. To our knowledge, our results are the first characterization of single-unit pre-ictal firing dynamics across the thalamocortical circuit in absence epilepsy. Our results argue that seizure-monitoring devices may be able to capitalize on seizure-by-seizure changes in pre-ictal activity to better predict seizure onset, and that the thalamus may be a source of clinically useful pre-ictal signatures.

Complexity and Frailty: Multiscale Entropy of Balance Dynamics During Quiet Standing and Dual-Task: The Mobilize Boston Study

2008 ◽  
Author(s):  
Hyun Gu Kang ◽  
Madalena Costa ◽  
Attila A. Priplata ◽  
Olga V. Starobinets ◽  
Ary L. Goldberger ◽  
...  
Keyword(s):  
Postural Control ◽  
Dual Task ◽  
Center Of Pressure ◽  
Balance Control ◽  
Community Dwelling ◽  
Multiscale Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Boston Study ◽  
Balance Dynamics

Balance control during standing is attributable to the complex, nonlinear interactions of multiple postural control systems, manifested as the highly irregular displacements in center of pressure (COP) during standing. Aging and associated frailty may result in the degradation of these complex interactions and manifest as a loss of complexity in COP dynamics. Furthermore, frail individuals may not be able to adapt to a superimposed stress that challenges balance, leading to falls. To test these hypotheses, data were analyzed from the MOBILIZE Boston Study, an ongoing population-based study of community-dwelling older adults. Each participant’s frailty phenotype (not frail, pre-frail, frail) was determined using the Fried et al. 2001 definition. 551 participants (age 77.9±5.5) stood on a balance platform, with or without concurrently performing serial subtractions. Complexity of balance dynamics over multiple time scales was quantified using multiscale entropy (MSE), a more sensitive measure of physiologic health than variance. Of the participants, 39% were pre-frail and 6% were frail. Baseline MSE was lower with each successive frailty condition (p<0.002). When performing the cognitive task, MSE was lowered similarly in all groups (p<0.001). Frailty was associated with a loss of complexity in the dynamics of postural sway, which may be due to the degradation of integrated postural control networks that enable upright stance. Performance of a dual-task further reduced this complexity. Cognitive distractions during standing may further compromise balance control in frail individuals, which may explain their increased fall risk.

scholarly journals The Decreased Complexity of Blood Pressure Dynamics Is Associated With Higher White Matter Lesions in Older Adults

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 213-213
Author(s):  
Xin Jiang ◽  
Xia Gao ◽  
Hui Zhang ◽  
Wuhong Deng ◽  
Wen Fu ◽  
...  
Keyword(s):  
Older Adults ◽  
White Matter ◽  
White Matter Lesions ◽  
Microvascular Disease ◽  
Underlying Mechanism ◽  
Multiscale Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Lower Complexity ◽  
Fazekas Scale

Abstract White matter lesions (WML) are highly prevalent in older adults and thought to represent cerebral microvascular disease, contributing to slow gait and dementia. Hypertension is associated with WML. However, the underlying mechanism of this association is unclear. The complex beat-to-beat BP fluctuations represent the influence of BP regulatory mechanisms over multiple time scales. The association between WML and abnormalities in BP regulation may be manifest as a loss of complexity in BP dynamics. The aim of this study is thus to explore the relationships between hypertension, BP complexity, and WML in older adults. Twenty-two older adults with hypertension (SBP>140 mmHg) and 19 age-matched older adults without hypertension (i.e., control) completed this study. Their whole-brain WML were assessed by two neurologists using the Fazekas Scale. Greater score reflects higher WML grade. Each participant completed a 10-minute BP assessment when sitting quietly following the MRI. The continuous SBP and DBP series were recorded, and the complexity of them was quantified using multiscale entropy (MSE). Lower MSE reflects lower complexity. Compared to the controls, hypertensives had significantly greater Fazekas scores (i.e., higher WML grade) (F=4.8, p=0.02) and lower complexity of SBP and DBP (F>3.7, p<0.01), after adjusting for age. Across two cohorts, those with lower SBP and DBP complexity had higher Fazekas score (r<-0.51, p<0.01), and this association was independent of age and group. These results suggest that WML are associated with a loss of complexity in BP dynamics. Future longitudinal studies are needed to examine the causal relationship between WML and BP.

Quantifying the dynamics of electroencephalographic (EEG) signals to distinguish alcoholic and non-alcoholic subjects using an MSE based K-d tree algorithm

2018 ◽  
Vol 63 (4) ◽  
pp. 481-490 ◽  
Author(s):  
Lal Hussain ◽  
Wajid Aziz ◽  
Sharjil Saeed ◽  
Saeed Arif Shah ◽  
Malik Sajjad A. Nadeem ◽  
...  
Keyword(s):  
Time Scales ◽  
Coarse Grained ◽  
Multiscale Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Operating Characteristics ◽  
Eeg Signals ◽  
Temporal Scales ◽  
Online Application ◽  
Time And Space Complexity

Abstract In this paper, we have employed K-d tree algorithmic based multiscale entropy analysis (MSE) to distinguish alcoholic subjects from non-alcoholic ones. Traditional MSE techniques have been used in many applications to quantify the dynamics of physiological time series at multiple temporal scales. However, this algorithm requires O(N2), i.e. exponential time and space complexity which is inefficient for long-term correlations and online application purposes. In the current study, we have employed a recently developed K-d tree approach to compute the entropy at multiple temporal scales. The probability function in the entropy term was converted into an orthogonal range. This study aims to quantify the dynamics of the electroencephalogram (EEG) signals to distinguish the alcoholic subjects from control subjects, by inspecting various coarse grained sequences formed at different time scales, using traditional MSE and comparing the results with fast MSE (fMSE). The performance was also measured in terms of specificity, sensitivity, total accuracy and receiver operating characteristics (ROC). Our findings show that fMSE, with a K-d tree algorithmic approach, improves the reliability of the entropy estimation in comparison with the traditional MSE. Moreover, this new technique is more promising to characterize the physiological changes having an affect at multiple time scales.

scholarly journals INTEREST GROUP SESSION—MEASUREMENT, STATISTICS, AND RESEARCH DESIGN: ANALYSIS OF DEVELOPMENTAL AND COMPLEX SYSTEMS: UNDERSTANDING THE DYNAMICS OF AGING

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S376-S376
Author(s):  
Xiao Yang ◽  
Nilam Ram ◽  
Nilam Ram
Keyword(s):  
Complex Systems ◽  
Dynamic Models ◽  
Initial Conditions ◽  
Developmental Trajectories ◽  
Dynamic Change ◽  
System Thinking ◽  
Multiscale Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Regulation Model

Abstract Aging is the product of numerous dynamic processes that span multiple domains of functioning (e.g., biological, psychological, social), multiple levels of analysis, and multiple time-scales. Scientific inquiry in many fields has benefited from articulation and analysis of complex systems. This symposium brings together a collection of papers that illustrate how dynamical systems modeling is contributing to both theory and understanding of aging. Yang and colleagues apply Boolean network approach to intensive longitudinal data to identify sequences of emotion and behavior that lead to a stable equilibrium, and suggest how that information can be used to design interventions that push individuals toward a healthier equilibrium. Rector and colleagues illustrate use of dynamic indicators and multiscale entropy measures as indicators of resilience and explain how those measures may be used in prediction of physical recovery. Brick highlights how sequence mining methods can be used to identify commonalities and differences in dynamic change, and how those patterns characterize and distinguish groups with respect to aging trajectories. Moulder and colleagues demonstrate how latent maximum Lyapunov exponents can be used to study sensitivity of individuals’ developmental trajectories to initial conditions. Boker and colleagues provide a general overview of how dynamic models, including an adaptive equilibrium regulation model, distinguish resilience to acute versus chronic stressors and patterns of regulation. Together these papers highlight the value complex system thinking can add to our understanding and optimization of aging.

MULTISCALE ENTROPY ANALYSIS OF TRAFFIC TIME SERIES

2013 ◽  
Vol 24 (02) ◽  
pp. 1350006 ◽  
Author(s):  
JING WANG ◽  
PENGJIAN SHANG ◽  
XIAOJUN ZHAO ◽  
JIANAN XIA
Keyword(s):  
Time Series ◽  
Coarse Grained ◽  
Multiscale Entropy ◽  
Permutation Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Entropy Analysis ◽  
Traffic Time Series ◽  
Real World Datasets ◽  
Physiologic Time Series

There has been considerable interest in quantifying the complexity of different time series, such as physiologic time series, traffic time series. However, these traditional approaches fail to account for the multiple time scales inherent in time series, which have yielded contradictory findings when applied to real-world datasets. Then multi-scale entropy analysis (MSE) is introduced to solve this problem which has been widely used for physiologic time series. In this paper, we first apply the MSE method to different correlated series and obtain an interesting relationship between complexity and Hurst exponent. A modified MSE method called multiscale permutation entropy analysis (MSPE) is then introduced, which replaces the sample entropy (SampEn) with permutation entropy (PE) when measuring entropy for coarse-grained series. We employ the traditional MSE method and MSPE method to investigate complexities of different traffic series, and obtain that the complexity of weekend traffic time series differs from that of the workday time series, which helps to classify the series when making predictions.

scholarly journals Reduced Data Dualscale Entropy Analysis of HRV Signals for Improved Congestive Heart Failure Detection

2014 ◽  
Vol 14 (5) ◽  
pp. 294-301 ◽  
Author(s):  
Srinivas Kuntamalla ◽  
Ram Gopal Reddy Lekkala
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Failure Detection ◽  
Multiscale Entropy ◽  
Multiple Time Scales ◽  
Elderly Subjects ◽  
Multiple Time ◽  
Entropy Analysis ◽  
Young And Elderly Subjects ◽  
Reduced Data

Abstract Heart rate variability (HRV) is an important dynamic variable of the cardiovascular system, which operates on multiple time scales. In this study, Multiscale entropy (MSE) analysis is applied to HRV signals taken from Physiobank to discriminate Congestive Heart Failure (CHF) patients from healthy young and elderly subjects. The discrimination power of the MSE method is decreased as the amount of the data reduces and the lowest amount of the data at which there is a clear discrimination between CHF and normal subjects is found to be 4000 samples. Further, this method failed to discriminate CHF from healthy elderly subjects. In view of this, the Reduced Data Dualscale Entropy Analysis method is proposed to reduce the data size required (as low as 500 samples) for clearly discriminating the CHF patients from young and elderly subjects with only two scales. Further, an easy to interpret index is derived using this new approach for the diagnosis of CHF. This index shows 100 % accuracy and correlates well with the pathophysiology of heart failure.

scholarly journals Multivariate and Multiscale Complexity of Long-Range Correlated Cardiovascular and Respiratory Variability Series

Entropy ◽  
2020 ◽  
Vol 22 (3) ◽  
pp. 315 ◽  
Author(s):  
Aurora Martins ◽  
Riccardo Pernice ◽  
Celestino Amado ◽  
Ana Paula Rocha ◽  
Maria Eduarda Silva ◽  
...  
Keyword(s):  
Time Series ◽  
Long Range ◽  
Time Scales ◽  
Multivariate Time Series ◽  
Multiscale Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Control Mechanisms ◽  
Long Range Correlations ◽  
Dynamical Complexity

Assessing the dynamical complexity of biological time series represents an important topic with potential applications ranging from the characterization of physiological states and pathological conditions to the calculation of diagnostic parameters. In particular, cardiovascular time series exhibit a variability produced by different physiological control mechanisms coupled with each other, which take into account several variables and operate across multiple time scales that result in the coexistence of short term dynamics and long-range correlations. The most widely employed technique to evaluate the dynamical complexity of a time series at different time scales, the so-called multiscale entropy (MSE), has been proven to be unsuitable in the presence of short multivariate time series to be analyzed at long time scales. This work aims at overcoming these issues via the introduction of a new method for the assessment of the multiscale complexity of multivariate time series. The method first exploits vector autoregressive fractionally integrated (VARFI) models to yield a linear parametric representation of vector stochastic processes characterized by short- and long-range correlations. Then, it provides an analytical formulation, within the theory of state-space models, of how the VARFI parameters change when the processes are observed across multiple time scales, which is finally exploited to derive MSE measures relevant to the overall multivariate process or to one constituent scalar process. The proposed approach is applied on cardiovascular and respiratory time series to assess the complexity of the heart period, systolic arterial pressure and respiration variability measured in a group of healthy subjects during conditions of postural and mental stress. Our results document that the proposed methodology can detect physiologically meaningful multiscale patterns of complexity documented previously, but can also capture significant variations in complexity which cannot be observed using standard methods that do not take into account long-range correlations.

scholarly journals Multiscale Dynamics of Blood Pressure Fluctuation Is Associated With White Matter Lesion Burden in Older Adults With and Without Hypertension: Observations From a Pilot Study

2021 ◽  
Vol 8 ◽  
Author(s):  
Xin Jiang ◽  
Yi Guo ◽  
Yue Zhao ◽  
Xia Gao ◽  
Dan Peng ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Older Adults ◽  
White Matter ◽  
Pilot Study ◽  
White Matter Lesions ◽  
Structural Mri ◽  
Multiscale Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Fazekas Scale

Background: White matter lesions (WMLs) are highly prevalent in older adults, and hypertension is one of the main contributors to WMLs. The blood pressure (BP) is regulated by complex underlying mechanisms over multiple time scales, thus the continuous beat-to-beat BP fluctuation is complex. The association between WMLs and hypertension may be manifested as diminished complexity of BP fluctuations. The aim of this pilot study is to explore the relationships between hypertension, BP complexity, and WMLs in older adults.Method: Fifty-three older adults with clinically diagnosed hypertension and 47 age-matched older adults without hypertension completed one MRI scan and one BP recording of 10–15 min when sitting quietly. Their cerebral WMLs were assessed by two neurologists using the Fazekas scale based on brain structural MRI of each of their own. Greater score reflected higher WML grade. The complexity of continuous systolic (SBP) and diastolic (DBP) BP series was quantified using multiscale entropy (MSE). Lower MSE reflected lower complexity.Results: Compared to the non-hypertensive group, hypertensives had significantly greater Fazekas scores (F > 5.3, p < 0.02) and lower SBP and DBP complexity (F > 8.6, p < 0.004). Both within each group (β < −0.42, p < 0.01) and across groups (β < −0.47, p < 0.003), those with lower BP complexity had higher Fazekas score. Moreover, complexity of both SBP and DBP mediated the influence of hypertension on WMLs (indirect effects > 0.25, 95% confidence intervals = 0.06 – 0.50).Conclusion: These results suggest that diminished BP complexity is associated with WMLs and may mediate the influence of hypertension on WMLs. Future longitudinal studies are needed to examine the causal relationship between BP complexity and WMLs.

scholarly journals Multiscale based nonlinear dynamics analysis of heart rate variability signals

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243441
Author(s):  
Syed Zaki Hassan Kazmi ◽  
Nazneen Habib ◽  
Rabia Riaz ◽  
Sanam Shahla Rizvi ◽  
Syed Ali Abbas ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Scales ◽  
Normal Sinus Rhythm ◽  
Autonomic Control ◽  
Multiscale Entropy ◽  
Permutation Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Cardiac Autonomic Control

Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach multiscale ACI (MACI) is proposed by incorporating multiple time scales for improving the classification ability of ACI. We evaluated the performance of MACI for classifying, normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation subjects. The findings reveal that MACI provided better classification between healthy and pathological subjects compared to ACI. We also compared MACI with other scale-based techniques such as multiscale entropy, multiscale permutation entropy (MPE), multiscale normalized corrected Shannon entropy (MNCSE) and multiscale permutation entropy (IMPE). The preliminary results show that MACI values are more stable and reliable than IMPE and MNCSE. The results show that MACI based features lead to higher classification accuracy.

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