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Digulescu A, Ioana C, Serbanescu A. Phase Diagram-Based Sensing with Adaptive Waveform Design and Recurrent States Quantification for the Instantaneous Frequency Law Tracking. SENSORS 2019; 19:s19112434. [PMID: 31141950 PMCID: PMC6603687 DOI: 10.3390/s19112434] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/14/2019] [Revised: 05/16/2019] [Accepted: 05/24/2019] [Indexed: 11/16/2022]
Abstract
Monitoring highly dynamic environments is a difficult task when the changes within the systems require high speed monitoring systems. An active sensing system has to solve the problem of overlapped responses coming from different parts of the surveyed environment. Thus, the need of a new representation space which separates the overlapped responses, is mandatory. This paper describes two new concepts for high speed active sensing systems. On the emitter side, we propose a phase-space-based waveform design that presents a unique shape in the phase space, which can be easily converted into a real signal. We call it phase space lobe. The instantaneous frequency (IF) law of the emitted signal is found inside the time series. The main advantage of this new concept is its capability to generate several distinct signals, non-orthogonal in the time/frequency domain but orthogonal within the representation space, namely the phase diagram. On the receiver side, the IF law information is estimated in the phase diagram representation domain by quantifying the recurrent states of the system. This waveform design technique gives the possibility to develop the high speed sensing methods, adapted for monitoring complex dynamic phenomena In our paper, as an applicative context, we consider the problem of estimating the time of flight in an dynamic acoustic environment. In this context, we show through experimental trials that our approach provides three times more accurate estimation of time of flight than spectrogram based technique. This very good accuracy comes from the capability of our approach to generate separable IF law components as well as from the quantification in phase diagram, both of them being the key element of our approach for high speed sensing.
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Affiliation(s)
- Angela Digulescu
- Department of Communications and Military Electronic Systems, Military Technical Academy "Ferdinand I", 050141 Bucharest, Romania.
| | - Cornel Ioana
- GIPSA-Lab, Université Grenoble Alpes, 38400 Saint Martin d'Hères, France.
| | - Alexandru Serbanescu
- Department of Communications and Military Electronic Systems, Military Technical Academy "Ferdinand I", 050141 Bucharest, Romania.
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Bosl WJ, Loddenkemper T, Nelson CA. Nonlinear EEG biomarker profiles for autism and absence epilepsy. ACTA ACUST UNITED AC 2017. [DOI: 10.1186/s40810-017-0023-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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Müller A, Kraemer JF, Penzel T, Bonnemeier H, Kurths J, Wessel N. Causality in physiological signals. Physiol Meas 2016; 37:R46-72. [DOI: 10.1088/0967-3334/37/5/r46] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Hripcsak G, Albers DJ, Perotte A. Parameterizing time in electronic health record studies. J Am Med Inform Assoc 2015; 22:794-804. [PMID: 25725004 PMCID: PMC6169471 DOI: 10.1093/jamia/ocu051] [Citation(s) in RCA: 42] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2014] [Revised: 11/08/2014] [Accepted: 12/22/2014] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Fields like nonlinear physics offer methods for analyzing time series, but many methods require that the time series be stationary-no change in properties over time.Objective Medicine is far from stationary, but the challenge may be able to be ameliorated by reparameterizing time because clinicians tend to measure patients more frequently when they are ill and are more likely to vary. METHODS We compared time parameterizations, measuring variability of rate of change and magnitude of change, and looking for homogeneity of bins of temporal separation between pairs of time points. We studied four common laboratory tests drawn from 25 years of electronic health records on 4 million patients. RESULTS We found that sequence time-that is, simply counting the number of measurements from some start-produced more stationary time series, better explained the variation in values, and had more homogeneous bins than either traditional clock time or a recently proposed intermediate parameterization. Sequence time produced more accurate predictions in a single Gaussian process model experiment. CONCLUSIONS Of the three parameterizations, sequence time appeared to produce the most stationary series, possibly because clinicians adjust their sampling to the acuity of the patient. Parameterizing by sequence time may be applicable to association and clustering experiments on electronic health record data. A limitation of this study is that laboratory data were derived from only one institution. Sequence time appears to be an important potential parameterization.
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Affiliation(s)
- George Hripcsak
- Department of Biomedical Informatics, Columbia University Medical Center, New York, USA Medical Informatics Services, NewYork-Presbyterian Hospital, New York, USA
| | - David J Albers
- Department of Biomedical Informatics, Columbia University Medical Center, New York, USA
| | - Adler Perotte
- Department of Biomedical Informatics, Columbia University Medical Center, New York, USA
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Bacci M, Vitalis A, Caflisch A. A molecular simulation protocol to avoid sampling redundancy and discover new states. Biochim Biophys Acta Gen Subj 2014; 1850:889-902. [PMID: 25193737 DOI: 10.1016/j.bbagen.2014.08.013] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Revised: 08/21/2014] [Accepted: 08/25/2014] [Indexed: 10/24/2022]
Abstract
BACKGROUND For biomacromolecules or their assemblies, experimental knowledge is often restricted to specific states. Ambiguity pervades simulations of these complex systems because there is no prior knowledge of relevant phase space domains, and sampling recurrence is difficult to achieve. In molecular dynamics methods, ruggedness of the free energy surface exacerbates this problem by slowing down the unbiased exploration of phase space. Sampling is inefficient if dwell times in metastable states are large. METHODS We suggest a heuristic algorithm to terminate and reseed trajectories run in multiple copies in parallel. It uses a recent method to order snapshots, which provides notions of "interesting" and "unique" for individual simulations. We define criteria to guide the reseeding of runs from more "interesting" points if they sample overlapping regions of phase space. RESULTS Using a pedagogical example and an α-helical peptide, the approach is demonstrated to amplify the rate of exploration of phase space and to discover metastable states not found by conventional sampling schemes. Evidence is provided that accurate kinetics and pathways can be extracted from the simulations. CONCLUSIONS The method, termed PIGS for Progress Index Guided Sampling, proceeds in unsupervised fashion, is scalable, and benefits synergistically from larger numbers of replicas. Results confirm that the underlying ideas are appropriate and sufficient to enhance sampling. GENERAL SIGNIFICANCE In molecular simulations, errors caused by not exploring relevant domains in phase space are always unquantifiable and can be arbitrarily large. Our protocol adds to the toolkit available to researchers in reducing these types of errors. This article is part of a Special Issue entitled "Recent developments of molecular dynamics".
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Affiliation(s)
- Marco Bacci
- University of Zurich, Department of Biochemistry, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland
| | - Andreas Vitalis
- University of Zurich, Department of Biochemistry, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.
| | - Amedeo Caflisch
- University of Zurich, Department of Biochemistry, Winterthurerstrasse 190, CH-8057 Zurich, Switzerland.
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Palivonaite R, Lukoseviciute K, Ragulskis M. Algebraic segmentation of short nonstationary time series based on evolutionary prediction algorithms. Neurocomputing 2013. [DOI: 10.1016/j.neucom.2013.05.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Abstract
The national adoption of electronic health records (EHR) promises to make an unprecedented amount of data available for clinical research, but the data are complex, inaccurate, and frequently missing, and the record reflects complex processes aside from the patient's physiological state. We believe that the path forward requires studying the EHR as an object of interest in itself, and that new models, learning from data, and collaboration will lead to efficient use of the valuable information currently locked in health records.
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Affiliation(s)
- George Hripcsak
- Biomedical Informatics, Columbia University, New York, NY 10027,
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Albers DJ, Hripcsak G. Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations. CHAOS (WOODBURY, N.Y.) 2012; 22:013111. [PMID: 22462987 PMCID: PMC3277606 DOI: 10.1063/1.3675621] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2010] [Accepted: 12/08/2011] [Indexed: 05/25/2023]
Abstract
This paper addresses how to calculate and interpret the time-delayed mutual information (TDMI) for a complex, diversely and sparsely measured, possibly non-stationary population of time-series of unknown composition and origin. The primary vehicle used for this analysis is a comparison between the time-delayed mutual information averaged over the population and the time-delayed mutual information of an aggregated population (here, aggregation implies the population is conjoined before any statistical estimates are implemented). Through the use of information theoretic tools, a sequence of practically implementable calculations are detailed that allow for the average and aggregate time-delayed mutual information to be interpreted. Moreover, these calculations can also be used to understand the degree of homo or heterogeneity present in the population. To demonstrate that the proposed methods can be used in nearly any situation, the methods are applied and demonstrated on the time series of glucose measurements from two different subpopulations of individuals from the Columbia University Medical Center electronic health record repository, revealing a picture of the composition of the population as well as physiological features.
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Affiliation(s)
- D J Albers
- Department of Biomedical Informatics, Columbia University, 622 West 168th Street, VC-5, New York, New York 10032, USA.
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Martini M, Kranz TA, Wagner T, Lehnertz K. Inferring directional interactions from transient signals with symbolic transfer entropy. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2011; 83:011919. [PMID: 21405725 DOI: 10.1103/physreve.83.011919] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/06/2010] [Indexed: 05/05/2023]
Abstract
We extend the concept of symbolic transfer entropy to enable the time-resolved investigation of directional relationships between coupled dynamical systems from short and transient noisy time series. For our approach, we consider an observed ensemble of a sufficiently large number of time series as multiple realizations of a process. We derive an index that quantifies the preferred direction of transient interactions and assess its significance using a surrogate-based testing scheme. Analyzing time series from noisy chaotic systems, we demonstrate numerically the applicability and limitations of our approach. Our findings obtained from an analysis of event-related brain activities underline the importance of our method to improve understanding of gross neural interactions underlying cognitive processes.
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Affiliation(s)
- Marcel Martini
- Department of Epileptology, University of Bonn, Bonn, Germany.
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Komalapriya C, Romano MC, Thiel M, Marwan N, Kurths J, Kiss IZ, Hudson JL. An automated algorithm for the generation of dynamically reconstructed trajectories. CHAOS (WOODBURY, N.Y.) 2010; 20:013107. [PMID: 20370262 DOI: 10.1063/1.3279680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
The lack of long enough data sets is a major problem in the study of many real world systems. As it has been recently shown [C. Komalapriya, M. Thiel, M. C. Romano, N. Marwan, U. Schwarz, and J. Kurths, Phys. Rev. E 78, 066217 (2008)], this problem can be overcome in the case of ergodic systems if an ensemble of short trajectories is available, from which dynamically reconstructed trajectories can be generated. However, this method has some disadvantages which hinder its applicability, such as the need for estimation of optimal parameters. Here, we propose a substantially improved algorithm that overcomes the problems encountered by the former one, allowing its automatic application. Furthermore, we show that the new algorithm not only reproduces the short term but also the long term dynamics of the system under study, in contrast to the former algorithm. To exemplify the potential of the new algorithm, we apply it to experimental data from electrochemical oscillators and also to analyze the well-known problem of transient chaotic trajectories.
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Affiliation(s)
- C Komalapriya
- Interdisciplinary Centre for Dynamics of Complex Systems, University of Potsdam, 14476 Potsdam, Germany.
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Cross DJ, Michaluk R, Gilmore R. Biological algorithm for data reconstruction. PHYSICAL REVIEW. E, STATISTICAL, NONLINEAR, AND SOFT MATTER PHYSICS 2010; 81:036217. [PMID: 20365842 DOI: 10.1103/physreve.81.036217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/05/2009] [Revised: 01/08/2010] [Indexed: 05/29/2023]
Abstract
An algorithm inspired by Genome sequencing is proposed which "reconstructs" a single long trajectory of a dynamical system from many short trajectories. This procedure is useful in situations when many data sets are available but each is insufficiently long to apply a meaningful analysis directly. The algorithm is applied to the Rössler and Lorenz dynamical systems as well as to experimental data taken from the Belousov-Zhabotinskii chemical reaction. Topological information was reliably extracted from each system and geometrical and dynamical measures were computed.
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Affiliation(s)
- Daniel J Cross
- Physics Department, Drexel University, Philadelphia, Pennsylvania 19104, USA
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Albers DJ, Hripcsak G. A statistical dynamics approach to the study of human health data: resolving population scale diurnal variation in laboratory data. PHYSICS LETTERS. A 2010; 374:1159-1164. [PMID: 20544004 PMCID: PMC2882798 DOI: 10.1016/j.physleta.2009.12.067] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2023]
Abstract
Statistical physics and information theory is applied to the clinical chemistry measurements present in a patient database containing 2.5 million patients' data over a 20-year period. Despite the seemingly naive approach of aggregating all patients over all times (with respect to particular clinical chemistry measurements), both a diurnal signal in the decay of the time-delayed mutual information and the presence of two sub-populations with differing health are detected. This provides a proof in principle that the highly fragmented data in electronic health records has potential for being useful in defining disease and human phenotypes.
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Affiliation(s)
- D. J. Albers
- Department of Biomedical Informatics, Columbia University, 622 W 168 St. VC-5, New York, NY 10032
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, 622 W 168 St. VC-5, New York, NY 10032
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Baptista MS, Maranhão DM, Sartorelli JC. Dynamical estimates of chaotic systems from Poincare recurrences. CHAOS (WOODBURY, N.Y.) 2009; 19:043115. [PMID: 20059211 DOI: 10.1063/1.3263943] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
We show a function that fits well the probability density of return times between two consecutive visits of a chaotic trajectory to finite size regions in phase space. It deviates from the exponential statistics by a small power-law term, a term that represents the deterministic manifestation of the dynamics. We also show how one can quickly and easily estimate the Kolmogorov-Sinai entropy and the short-term correlation function by realizing observations of high probable returns. Our analyses are performed numerically in the Henon map and experimentally in a Chua's circuit. Finally, we discuss how our approach can be used to treat the data coming from experimental complex systems and for technological applications.
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Affiliation(s)
- M S Baptista
- Institute for Complex Systems and Mathematical Biology, King's College, University of Aberdeen, AB24 3UE Aberdeen, United Kingdom
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