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PubMed2003

Predicting defibrillation success by 'genetic' programming in patients with out-of-hospital cardiac arrest.

BACKGROUND: In some patients with ventricular fibrillation (VF) there may be a better chance of successful defibrillation after a period of chest compression and ventilation before the defibrillation attempt. It is therefore important to know whether a defibrillation attempt will be successful. The predictive power of a model developed by 'genetic' programming (GP) to predict defibrillation success was studied. METHODS AND RESULTS: 203 defibrillations were administered in 47 patients with out-of-hospital cardiac arrest due to a cardiac cause. Maximal amplitude, a total energy of power spectral density, and the Hurst exponent of the VF electrocardiogram (ECG) signal were included in the model developed by GP. Positive and negative likelihood ratios of the model for testing data were 35.5 and 0.00, respectively. Using a model developed by GP on the complete database, 120 of the 124 unsuccessful defibrillations would have been avoided, whereas all of the 79 successful defibrillations would have been administered. CONCLUSION: The VF ECG contains information predictive of defibrillation success. The model developed by GP, including data from the time-domain, frequency-domain and nonlinear dynamics, could reduce the incidence of unsuccessful defibrillations.

Aged↗

PubMed2002

Effects of hypoxia on the complexity of respiratory patterns during maturation.

During hypoxic gasping, the hypoxic neurogram has a steeper rate of rise, an augmented amplitude, and a shorter duration than is seen during eupnea. Because hypoxia reduces neural activity, we hypothesized that gasping would be characterized by low complexity (irregularity) values compared with eupnea in piglets. In this study, we define and quantify changes in the complexity of the phrenic neurogram, the output of the respiratory neural network in piglets using the approximate entropy (ApEn) method which provides a model independent measure of the complexity of the phrenic neurogram. The phrenic neurogram in vagotomized, peripherally chemodenervated, decerebrated piglets was recorded from the C5 phrenic nerve during eupnea and gasping at four postnatal ages; 3-6 days of age (n=8), 7-13 days of age (n=3), 15-21 days of age (n=4), 29-35 days of age (n=10). Nonlinear dynamical analysis of the phrenic neurogram was performed using the approximate entropy method. The mean approximate entropy values for a recording of 5 consecutive breaths during eupnea and 6-29 consecutive breaths during gasping for each piglet in each group during eupnea was calculated. Our results suggested that the mean approximate entropy values for the 3-6 days age group were 1.46+/-0.003 during eupnea and 0.85+/-0.001 during hypoxic gasping. For the 7-13 days age group, the mean approximate entropy values were 1.35+/-0.009 during eupnea and 1.00+/-0.001 during hypoxic gasping. For the 15-21 days age group, they were 1.33+/-0.005 during eupnea and 0.94+/-0.001 during hypoxic gasping. Finally, for the 29-35 days age group, they were 1.38+/-0.002 during eupnea and 0.93+/-0.001 during hypoxic gasping. The shift from eupnea to gasping caused a drastic drop in the mean values of the approximate entropy values at each of these four age groups. These differences in the complexity values of the phrenic neurogram between eupnea and gasping are statistically different at each age group (p<0.001). These findings suggest that during hypoxic gasping, regardless of degree of development, the output of the central pattern generator becomes less complex probably because hypoxia reduces the neural activity.

Age Factors↗

PubMed1997

Predictive value for future arrhythmic events of fractal dimension, a measure of time clustering of ventricular premature complexes, after myocardial infarction. CAST Investigators. Cardia Arrhythmia Suppression Trial.

OBJECTIVES: Our objective was to test fractal dimension (D), a measure of clustering of ventricular premature complexes (VPCs), on entry Holter recording as a predictor of future arrhythmic death and other-cause mortality in postinfarction patients in the Cardiac Arrhythmic Suppression Trial (CAST). BACKGROUND: Nonlinear dynamic methods of signal processing are being applied in medicine to provide new insights into apparently "chaotic" biologic events, including cardiac arrhythmias. One such application is the derivation of a fractal D to describe the clustering of VPCs in time. METHODS: Baseline Holter recordings were analyzed in blinded manner for 484 patients: 237 died or had a resuscitated cardiac arrest during follow-up, and 247 matched patients had no events. Fractal D, measured in four ways, was assessed as a predictor using Cox regression. RESULTS: One measure of D (high resolution D) was a significant univariate (relative hazard ratio 0.79 per SD change, p = 0.011) and multivariate (hazard ratio 0.75, p = 0.046) predictor of arrhythmic death but not other death (univariate p = 0.95, relative hazard 0.95, p = 0.66). Fractal D was greater (VPCs less clustered) in those patients free of arrhythmic events. On subgroup analysis, the predictive value of D resided in the randomized patient group (i.e., those who showed VPC suppression during initial antiarrhythmic drug titration and were randomized to blinded therapy with active drug or placebo) (multivariate hazard ratio 0.57, p = 0.001). CONCLUSIONS: A high resolution fractal D was predictive of arrhythmic (but not nonarrhythmic) death in a large postinfarction cohort. Further study of this new signal processing approach to ambulatory electrocardiographic recording will be of interest.

Aged↗

PubMed2000

The life of form. Emergent patterns of morphological transformation.

The problem of biological form remains unresolved despite the known details of gene activities in embryonic development. Segmentation is discussed in connection with observed gene activities and the question of generic regularities across phyla. The sciences of complexity and nonlinear dynamics are considered as contexts for the exploration of constraints at both the level of genetic networks and morphogenesis. A new model of morphogenetic constraints coupled to dynamic attractors is proposed as an approach to the question--how to unite the contingencies of evolutionary genetic changes with the emergent regularities of organismic morphologies?

Animals↗

PubMedDate not supplied

Studies of reproductive cyclicity: evaluation of computer modeling as a tool.

Assessment of toxic effects on human reproductive function using fetal health or teratogenic criteria presumes the conditions of parental fertility. Toxins that compromise fecundity through derangement of the menstrual cycle may require quite different models and criteria. Nonlinear dynamic interactions of the hormonal and morphological components of the menstrual cycle add to the difficulty of such studies. With appropriate mathematical models, computer simulation can provide a useful guide to the design and conduct of in vivo experiment.

Computer Simulation↗

PubMed1999

Nonlinear behavior of vocal fold vibration: the role of coupling between the vocal folds.

Coupling between the vocal folds is one of the nonlinear mechanisms allowing regulation and synchronization of mucosal vibration. The purpose of this study was to establish that modulations such as diplophonia and abnormalities observed in vocal signals that may be observed in some cases of laryngeal pathology can be considered as nonlinear behavior due to the persistence of some physical interaction (coupling). An experimental model using excised porcine larynx was designed to create tension asymmetry between the vocal folds and to obtain vocal signals with modulations. Signals were analyzed by spectral analysis and the phase portrait method. Results were compared with computer-generated synthetic signals corresponding to nonlinear combinations of sinusoid signals. Under these conditions, evidence of nonlinear behavior was detected in 85% of experimental signals. These findings were interpreted as a demonstration of vocal fold interaction. Based on these findings, the authors conclude that (1) coupling must be taken into account in physical models of laryngeal physiology, and that (2) methods of nonlinear dynamics may be used for objective voice analysis.

Animals↗

PubMed1996

Fast Second Order Learning Algorithm for Feedforward Multilayer Neural Networks and its Applications.

The paper presents the efficient training program of multilayer feedforward neural networks. It is based on the best second order optimization algorithms including variable metric and conjugate gradient as well as application of directional minimization in each step. Its efficiency is proved on the standard tests, including parity, dichotomy, logistic and two-spiral problems. The application of the algorithm to the solution of higher dimensionality problems like deconvolution, separation of sources and identification of nonlinear dynamic plant are also given and discussed. It is shown that the appropriately trained neural network can be used for the nonconventional solution of these standard signal processing tasks with satisfactory accuracy. The results of numerical experiments are included and discussed in the paper. Copyright 1996 Elsevier Science Ltd.

Journal Article↗

PubMed1997

Linsker-type Hebbian Learning: A Qualitative Analysis on the Parameter Space.

We developed a new method to relate the choice of system parameters to the outcomes of the unsupervised learning process in Linsker's multi-layer network model. The behavior of this model is determined by the underlying nonlinear dynamics that are parameterized by a set of parameters originating from the Hebb rule and the arbor density of the synapses. These parameters determine the presence or absence of a specific receptive field (or connection pattern) as a saturated fixed point attractor of the model. We derived a necessary and sufficient condition to test whether a given saturated weight vector is stable or not for any given set of system parameters, and used this condition to determine the whole regime in the parameter space over which the given connection pattern is stable. The parameter space approach allows us to investigate the relative stability of the major receptive fields reported in Linsker's simulation, and to demonstrate the crucial role played by the localized arbor density of synapses between adjacent layers. The method presented here can be employed to analyze other learning and retrieval models that use the limiter function as the constraint controlling the magnitude of the weight or state vectors. Copyright 1997 Elsevier Science Ltd.

Journal Article↗

PubMed2000

Peripheral blood stem cell collection: the interaction of technology, procedure, and biological factors.

Centrifugal technology, continuous flow and discontinuous flow, has served as the technology platform for extracting cell concentrates of interest from peripheral blood (PB) for patient therapy for the past 35-40 yr. Models for procedure outcome exist for collection of normal donor (ND) platelet and granulocyte concentrates that integrate: (1) biological variables (pre-procedure PB cell concentration, the total circulating quantity of cells, donor/patient blood volume (BV)), (2) device efficiency, and (3) procedure parameters such as total blood processed (TBP), and in the case of cytoreductions - the volume collected. (cf. Hester J, Kellogg R, Mulzet A, et al., Blood (54) (1979) 254; Hester J, Ventura G, J Clin Apheresis (4) (1988) 188.) To date, no predictive CD34+ yield algorithm integrating these three variables has been formulated that could be applied prospectively for individual ND or patients (PT). There are economic, toxicity and statistical comparison benefits to be derived from generating such an algorithm.A small pilot study is presented with a brief review of current publications that suggest the circulating quantity of CD34+ cells available to be collected and the quantity mobilized during leukapheresis are the major contributing factors to CD34+ yield, somewhat obscuring the role of the total blood processed (TBP). Intraprocedure CD34+ cell mobilization, incompletely characterized to date, appears to be a dynamic nonlinear process, as the harvested yield does not rise proportionally as the fraction of BV processed increases. And, like the pre-procedure PB CD34+ concentration and total circulating quantity, CD34+ mobilization during leukapheresis probably relates to prior treatment and the priming regimen. Studies that provide: (1) separate analyses of PT populations divided according to chemotherapy toxicity factors; (2) design and implementation of optimal priming regimens with respect to dose 'intensity' of both growth factors and chemotherapy; and (3) standardization of laboratory assays of CD34+ enumeration seem essential to generating a predictive algorithm.

Algorithms↗

PubMed1992

Broadband temporal stimuli decrease the integration time of neurons in cat striate cortex.

We have studied the responses of striate cortical neurons to stimuli whose contrast is modulated in time by either a single sinusoid or by the sum of eight sinusoids. The sum-of-sinusoids stimulus resembles white noise and has been used to study the linear and nonlinear dynamics of retinal ganglion cells (Victor et al., 1977). In cortical neurons, we have found different linear and second-order responses to single-sinusoid and sum-of-sinusoids inputs. Specifically, while the responsivity near the optimal temporal frequency is lower for the sum-of-sinusoids stimulus, the responsivity at higher temporal frequencies is relatively greater. Along with this change in the response amplitudes, there is a systematic change in the time course of responses. For complex cells, the integration time, the effective delay due to a combination of actual delays and low-pass filter stages, changes from a median of 85 ms with single sinusoids to 57 ms with a sum of sinusoids. For simple cells, the integration times for single sinusoids range from 44-100 ms, but cluster tightly around 40 ms for the sum-of-sinusoids stimulus. The change in time constant would argue that the increased sensitivity to high frequencies cannot be explained by a static threshold, but must be caused by a fundamental alteration in the response dynamics. These effects are not seen in the retina (Shapley & Victor, 1981) and are most likely cortical in origin.

Animals↗

PubMedDate not supplied

A neural model of foveal light adaptation and afterimage formation.

Psychophysical research has documented the existence of three processes in light adaptation: a fast subtractive process, a divisive process that is fast at light onset and slower at light offset, and a very slow subtractive process (Hayhoe et al., 1987). In the neural model developed here, the fast subtractive process is identified with horizontal cell feedback onto cones and the divisive process with amacrine cell feedback onto bipolar cells. The very slow subtractive process is identified with the modulatory feedback circuit from amacrines via interplexiform cells to horizontal cells. A nonlinear dynamical model is developed incorporating these aspects of retinal circuitry along with both ON- and OFF-center M and P pathways. This model is shown to account for many aspects of foveal light adaptation, including negative afterimage formation, and to explain a number of the physiological differences between M and P ganglion cells, including their differing contrast-response functions.

Adaptation, Physiological↗

PubMed1997

Role of personality self-organization in development of mental order and disorder.

Normal and abnormal personality development can be quantified in terms of 15 specific steps in the self-organization of character as a complex adaptive system. Character is measured as three dimensions of Self-directedness, Cooperativeness, and Self-transcendence, each with five components corresponding to steps in personality development. Each of these steps is differentially influenced by heritable temperament dimensions, antecedent steps in character development, and life experiences. Predictions about the nonlinear dynamics of personality development, such as equifinality and multifinality, are confirmed in longitudinal data about individuals representative of the general population. The stepwise development of character determines large differences between individuals in their risk of psychopathology, as well as varying degrees of maturity and health.

Child↗

PubMed2004

Oscillating fluorescence in an unstable colloidal dispersion of CdSe/ZnS core/shell quantum dots.

Fluorescence oscillation is observed in an ensemble of colloidal CdSe/ZnS core/shell quantum dots (QDs) dispersed in nonpolar solvent under continuous irradiation. The QDs dispersed in toluene gradually aggregate and change their fluorescence intensity, even in the dark. During the aggregation, the QD/toluene suspension is unstable, that is, overdispersed. The fluorescence oscillation is found only in this unstable state before the system reaches steady state. In addition, the aggregation rate is promoted by irradiation and strongly correlates with the oscillation amplitude. Our experimental results indicate that the dispersion instability plays an important role in both linear and nonlinear dynamics of the fluorescence. It is inferred from the experimental results and previous studies that the complex time evolution of fluorescence in the QD/toluene dispersion is possibly due to adsorption and desorption of surface ligand molecules over the course of QD aggregation.

Journal Article↗

PubMed2000

Noise-driven neocortical interaction: a simple generation mechanism for complex neuron spiking.

We discuss a generic scenario along which complex spiking behavior evolves in biologically realistic neural networks. Our nonlinear dynamics approach is based directly on rat neocortical in vitro recordings. Using this experimental data, we obtain a full overview on the possible spiking behaviors of pyramidal neurons that are engaged in binary interactions. Universality arguments imply that the observed spiking behaviors are largely independent from the specific properties of individual neurons; theoretical arguments and numerical experiments indicate that they should be observable in in vivo neocortical neuron networks.

Animals↗

PubMed2003

Time, complex systems, and public policy: a theoretical foundation for adaptive policy making.

Can public policy development and implementation be improved by closely tracking and coordinating its timing with that of the regulated sector? Built-in asynchronies between political process and regulated sectors or activities can produce unintended disruptions in rates of economic change and development undermining the original intent of the policy or regulatory action. Such events sometimes lead to unexpected future disruptions as well. A policy approach is needed that adaptively ties the right mix of resources and regulatory activity to the timing of particular stages of economic development or growth associated with a particular industry. These public policy timing problems are explored using a new theory based on the concepts of "time-ecology," "heterochrony," and "temporal signature." The full range of linear and nonlinear time-space web linkages (electronic, selling and buying, technology transfer are examples) in an government-industry cluster between political, economic, and other elements creates an interconnected ecology-a time-ecology-of unique, more or less intense, and often complex rhythmic pulses occuring in parallel or in a punctuated way, and entrained with or influencing each other across multiple time scales flowing into the future. Each organizational structure is situated in its own past, present, and future in a unique way (time signature). Outside linkages mutually influence structuration by varying rates of development and growth (heterochrony). Nonlinear dynamics may be involved in these interactions. This whole process occurs on linked government/industry adaptive landscapes. The approach detailed in this paper may be applicable to organizational and time related issues in other disciplines. The paper ends with suggestions for testing the theory.

Adaptation, Psychological↗

PubMed2005

Human cognition and a pile of sand: a discussion on serial correlations and self-organized criticality.

Recently, G. C. Van Orden, J. G. Holden, and M. T. Turvey (2003) proposed to abandon the conventional framework of cognitive psychology in favor of the framework of nonlinear dynamical systems theory. Van Orden et al. presented evidence that "purposive behavior originates in self-organized criticality" (p. 333). Here, the authors show that Van Orden et al.'s analyses do not test their hypotheses. Further, the authors argue that a confirmation of Van Orden et al.'s hypotheses would not have constituted firm evidence in support of their framework. Finally, the absence of a specific model for how self-organized criticality produces the observed behavior makes it very difficult to derive testable predictions. The authors conclude that the proposed paradigm shift is presently unwarranted.

Cognition↗

PubMed2001

Exploring complex networks.

The study of networks pervades all of science, from neurobiology to statistical physics. The most basic issues are structural: how does one characterize the wiring diagram of a food web or the Internet or the metabolic network of the bacterium Escherichia coli? Are there any unifying principles underlying their topology? From the perspective of nonlinear dynamics, we would also like to understand how an enormous network of interacting dynamical systems-be they neurons, power stations or lasers-will behave collectively, given their individual dynamics and coupling architecture. Researchers are only now beginning to unravel the structure and dynamics of complex networks.

Models, Theoretical↗

PubMed1993

Chaos reduces species extinction by amplifying local population noise.

In the mid-1970s, theoretical ecologists were responsible for stimulating interest in nonlinear dynamics and chaos. Ironically, the importance of chaos in ecology itself remains controversial. Proponents of ecological chaos point to its ubiquity in mathematical models and to various empirical findings. Sceptics maintain that the models are unrealistic and that the experimental evidence is equally consistent with stochastic models. More generally, it has been argued that interdemic selection and/or enhanced rates of species extinction will eliminate populations and species that evolve into chaotic regions of parameter space. Fundamental to this opinion is the belief that violent oscillations and low minimum population densities are inevitable correlates of the chaotic state. In fact, rarity is not a necessary consequence of complex dynamical behaviour. But even when chaos is associated with frequent rarity, its consequences to survival are necessarily deleterious only in the case of species composed of a single population. Of course, the majority of real world species (for example, most insects) consist of multiple populations weakly coupled by migration, and in this circumstance chaos can actually reduce the probability of extinction. Here we show that although low densities lead to more frequent extinction at the local level, the decorrelating effect of chaotic oscillations reduces the degree of synchrony among populations and thus the likelihood that all are simultaneously extinguished.

Ecology↗