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.

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 Multiple time-scales and the developmental dynamics of social systems

2012 ◽  
Vol 367 (1597) ◽  
pp. 1802-1810 ◽  
Author(s):  
Jessica C. Flack
Keyword(s):  
Time Scales ◽  
Social Systems ◽  
Coarse Grained ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Collective Dynamics ◽  
Individual Level ◽  
Levels Of Organization ◽  
The Individual ◽  
Developmental Dynamics

To build a theory of social complexity, we need to understand how aggregate social properties arise from individual interaction rules. Here, I review a body of work on the developmental dynamics of pigtailed macaque social organization and conflict management that provides insight into the mechanistic causes of multi-scale social systems. In this model system coarse-grained, statistical representations of collective dynamics are more predictive of the future state of the system than the constantly in-flux behavioural patterns at the individual level. The data suggest that individuals can perceive and use these representations for strategical decision-making. As an interaction history accumulates the coarse-grained representations consolidate. This constrains individual behaviour and provides the foundations for new levels of organization. The time-scales on which these representations change impact whether the consolidating higher-levels can be modified by individuals and collectively. The time-scales appear to be a function of the ‘coarseness’ of the representations and the character of the collective dynamics over which they are averages. The data suggest that an advantage of multiple timescales is that they allow social systems to balance tradeoffs between predictability and adaptability. I briefly discuss the implications of these findings for cognition, social niche construction and the evolution of new levels of organization in biological systems.

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 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.

scholarly journals AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3144
Author(s):  
Rongheng Lin ◽  
Fangchun Yang ◽  
Mingyuan Gao ◽  
Budan Wu ◽  
Yingying Zhao
Keyword(s):  
Time Scales ◽  
User Behavior ◽  
Department Of Energy ◽  
Abnormal Behavior ◽  
Coarse Grained ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Clustering Methods ◽  
Detection Approach ◽  
Power Load

With the rapid growth of Smart Grid, electricity load analysis has become the simplest and most effective way to divide user groups and understand user behavior. This paper proposes an AUD-MTS (Abnormal User Detection approach based on power load multi-step clustering with Multiple Time Scales). Firstly, we combine RBM (Restricted Boltzmann Machine) hidden feature learning with K-Means clustering to extract typical load patterns in the short-term. Secondly, time scale conversion is performed so that the analysis subject can be transformed from load pattern to user behavior. Finally, a two-step clustering in long-term is adopted to divide users from both coarse-grained and fine-grained dimensions so as to detect abnormal users referring to customized OutlierIndex. Experiments are conducted using annual 24-point power load data of American users in all states. The accuracy of clustering methods in AUD-MTS reaches 87.5% referring to the 16 commercial building types defined by the U.S. Department of Energy, which outperforms other common clustering algorithms on AMI (Advanced Metering Infrastructure). After that, the OutlierIndex score of AUD-MTS can be increased by 0.16 compared with other outlier detection algorithms, which shows that the proposed method can detect abnormal users precisely and efficiently. Furthermore, we summarized possible causes including federal holidays, climate zones and summertime that may lead to abnormal behavior changes and discussed countermeasures respectively, which accounts for 82.3% of anomalies. The rest may be potential electricity stealing users, which requires further investigation.

scholarly journals Cardiovascular and Cardiorespiratory Signals Complexity Analysis Using Different Techniques

2021 ◽  
Author(s):  
Kirti Singh ◽  
Indu Saini ◽  
Neetu Sood
Keyword(s):  
Time Series ◽  
Time Scales ◽  
Complexity Analysis ◽  
Multiscale Entropy ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Short Term ◽  
Dynamic Complexity ◽  
Long Range Correlations ◽  
Biological Time

In recent decades, the concept of complex physiological systems has become more and more popular. The evaluation of the biological time series' dynamic complexity is an essential subject with possible applications such as the characterization of physiological states i.e. HRV, BP, and RESP signals and pathological disorders to the measurement of diagnostic parameters. The convergence of several physiological regulation processes is the cause of heterogeneity in cardiovascular time series, that consider many factors and function over several time scales, resulting not only the presence of short-term dynamics but also the coexistence of long-range correlations in various physiological signals. The most popular approach to evaluating the dynamic complexity and irregularity of time series over multiple time scales is entropy based analysis. The most used approach is multiscale entropy (MSE) and refined MSE (RMSE). It is then added to the heart period time series, respiration time series, and blood pressure time series, measured in young subjects and old subjects under resting conditions. This research applies to short-term cardiovascular and cardiorespiratory variability documents that LMSE can better describe physiological processes' behavior causing biological oscillations at various time scales than RMSE.

SOME REMARKS ON THE MULTIPLE TIME SCALES OF DIFFUSION IN MINERALS AND MELTS

2018 ◽  
Author(s):  
Yan Liang ◽  
◽  
Daniele J. Cherniak ◽  
Chenguang Sun
Keyword(s):  
Time Scales ◽  
Multiple Time Scales ◽  
Multiple Time

scholarly journals Are We Doing ‘Systems’ Research? An Assessment of Methods for Climate Change Adaptation to Hydrohazards in a Complex World

2019 ◽  
Vol 11 (4) ◽  
pp. 1163 ◽  
Author(s):  
Melissa Bedinger ◽  
Lindsay Beevers ◽  
Lila Collet ◽  
Annie Visser
Keyword(s):  
Climate Change ◽  
Human Nature ◽  
Climate Change Adaptation ◽  
Time Scales ◽  
Spatial Scales ◽  
Local Context ◽  
Multiple Time Scales ◽  
Future Research ◽  
Multiple Time ◽  
Systems Research

Climate change is a product of the Anthropocene, and the human–nature system in which we live. Effective climate change adaptation requires that we acknowledge this complexity. Theoretical literature on sustainability transitions has highlighted this and called for deeper acknowledgment of systems complexity in our research practices. Are we heeding these calls for ‘systems’ research? We used hydrohazards (floods and droughts) as an example research area to explore this question. We first distilled existing challenges for complex human–nature systems into six central concepts: Uncertainty, multiple spatial scales, multiple time scales, multimethod approaches, human–nature dimensions, and interactions. We then performed a systematic assessment of 737 articles to examine patterns in what methods are used and how these cover the complexity concepts. In general, results showed that many papers do not reference any of the complexity concepts, and no existing approach addresses all six. We used the detailed results to guide advancement from theoretical calls for action to specific next steps. Future research priorities include the development of methods for consideration of multiple hazards; for the study of interactions, particularly in linking the short- to medium-term time scales; to reduce data-intensivity; and to better integrate bottom–up and top–down approaches in a way that connects local context with higher-level decision-making. Overall this paper serves to build a shared conceptualisation of human–nature system complexity, map current practice, and navigate a complexity-smart trajectory for future research.

scholarly journals A multistate model incorporating estimation of excess hazards and multiple time scales

2021 ◽  
Vol 40 (9) ◽  
pp. 2139-2154
Author(s):  
Caroline E. Weibull ◽  
Paul C. Lambert ◽  
Sandra Eloranta ◽  
Therese M. L. Andersson ◽  
Paul W. Dickman ◽  
...  
Keyword(s):  
Time Scales ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Multistate Model
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