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At least 757 records · Page 42Linked to original sources

PubMed2005

Determining variability of ophthalmic arterial Doppler signals using Lyapunov exponents.

The new method presented in this study was directly based on the consideration that ophthalmic arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Multilayer perceptron neural network (MLPNN) architecture was formulated and used as a basis for determining variabilities such as stenosis, ocular Behcet disease, and uveitis disease in the physical state of ophthalmic arterial Doppler signals. The computed Lyapunov exponents of the ophthalmic arterial Doppler signals were used as inputs of the MLPNN. Receiver operating characteristic (ROC) curve was used to assess the performance of the detection process. The ophthalmic arterial Doppler signals were classified with the accuracy varying from 93.75% to 97.06%. The results confirmed that the proposed MLPNN trained with Levenberg-Marquardt algorithm has potential in detecting stenosis, Behcet disease and uveitis disease in ophthalmic arteries.

Adult↗

PubMed2005

Catastrophe theory model for decelerations of fetal heart rate.

BACKGROUND: Decelerations of fetal heart rate (FHR) are the results of many pathophysiological factors that modulate the intrinsic rate of the heart. FHR change is a complex phenomenon and can be viewed as a type of nonlinear dynamic system. This paper presents a qualitative model of FHR decelerations based on catastrophe theory, especially to account for abrupt heart rate changes seen in variable decelerations. METHODS: One of the elementary catastrophes, the cusp catastrophe, was used to model FHR changes seen in variable, late, and early decelerations. RESULTS: Catastrophe theory describes typical variable decelerations as an abrupt response to a slowly changing force of attack (uterine contraction) and physiological defense. Because of its three-dimensionality and topological features, the cusp catastrophe model provides a qualitatively satisfying description of FHR decelerations. CONCLUSION: The organized complexity of living systems offers the best hope for the application of catastrophe theory. The nature of the elementary catastrophes will be essential knowledge in the mathematical analysis of biology.

Female↗

PubMed2006

[Modelling a penicillin fed-batch fermentation using least squares support vector machines].

The biochemical processes are usually characterized as seriously time varying and nonlinear dynamic systems. Building their first-principle models are very costly and difficult due to the absence of inherent mechanism and efficient on-line sensors. Furthermore, these detailed and complicated models do not necessary guarantee a good performance in practice. An approach via least squares support vector machines (LS-SVM) based on Pensim simulator is proposed for modelling the penicillin fed-batch fermentation process, and the adjustment strategy for parameters of LS-SVM is presented. Based on the proposed modelling method, the predictive models of penicillin concentration, biomass concentration and substrate concentration are obtained by using very limited on-line measurements. The results show that the models established are more accurate and efficient, and suffice for the requirements of control and optimization for biochemical processes.

Biomass↗

PubMed2002

Changes in brain complexity during valproate treatment in patients with partial epilepsy.

OBJECTIVE: The effect of valproate (VPA) on human electroencephalography (EEG) was studied using nonlinear dynamics analysis to investigate changes in brain complexity. METHODS: We propose a spatial linear mode complexity (SLMC) measure to quantify the complexity of spatial linear modes in multichannel EEGs. Nine patients with complex partial seizures who had not previously been exposed to antiepileptic drugs (AEDs) were included in this study. Eighteen-channel EEG data were collected before and after VPA therapy. Changes in brain complexity were examined using the proposed SLMC measure, which reflects brain complexity. Fifteen normal, healthy subjects were included as a control group. To compare SLMC with spectral analysis, we performed spectral analysis within the conventional frequency bands. RESULTS: Spectral analysis showed that the patient group had decreased relative power of the alpha2 band in the T7, P3, O1 and C4 leads before VPA treatment and an increased relative theta power in the O1 lead relative to the control group. However, no significant changes occurred in any lead at any frequency band after VPA treatment. The mean SLMC value was significantly lower in the patient group before treatment than in the control group (p = 0.026). The average SLMC value for all patients increased after treatment and neared that of the control group, although statistical significance was not attained (p = 0.074). CONCLUSIONS: These results suggest that epilepsy patients have interictal abnormalities that are demonstrated by reduced brain complexity, and that VPA partially reverses this trend. Nonlinear analysis of EEG data may be useful in evaluating the effect of AEDs.

Adolescent↗

PubMed2000

[Clinical significance of calculating the coefficients of Lyapunov in the objective assessment of dysphonia].

Measurement of the vibration instability by means of jitter in cases of dysphonia fails to take into account certain features of the vocal signal, such as modulation or bitonatily. These abnormalities appear in jitter measurements as instability, without allowing any differentiation from random instability (to give 'true' instability). The authors suggest the use of the coefficient of Lyapunov, a measurement which is used for nonlinear dynamics. The vocal product of 179 normal and dysphonic subjects has been analysed. The coefficient of Lyapunov and the jitter were measured during the stable period of a sustained 'a' vowel. For the coefficient of Lyapunov, the authors used the algorythm which has been published previously [Giovanni et al, J. Voice 1999;13:341-354]. The measurements were compared with the perceptive analysis of the reading voice by a panel, and divided into four grade (G0 to G3). The coefficient of Lyapunov appeared to be more relevant than the jitter in distinguishing the various perceived grades. Precise recognition of the grade was obtained in 46% using the coefficient of Lyapunov as against only 36% using jitter. The addition of this new index to the existing puriparametric methods of voice analysis seems to be promising.

Acoustics↗

PubMed1999

Impedance characteristics of a neuromusculoskeletal model of the human arm I. Posture control.

The mechanical impedance of neuromusculoskeletal models of the human arm is studied in this paper. The model analysis provides a better understanding of the contributions of possible intrinsic and reflexive components of arm impedance, makes clear the limitations of second-order mass-viscosity-stiffness models and reveals possible task effects on the impedance. The musculoskeletal model describes planar movements of the upper arm and forearm, which are moved by six lumped muscles with nonlinear dynamics. The motor control system is represented by a neural network which combines feedforward and feedback control. It is optimized for the control of movements or for posture control in the presence of external forces. The achieved impedance characteristics depend on the conditions during the learning process. In particular, the impedance is adapted in a suitable way to the frequency content and direction of external forces acting on the hand during an isometric task. The impedance characteristics of a model, which is optimized for movement control, are similar to experimental data in the literature. The achieved stiffness is, to a large extent, reflexively determined whereas the approximated viscosity is primarily due to intrinsic attributes. It is argued that usually applied Hill-type muscle models do not properly represent intrinsic muscle stiffness.

Adaptation, Physiological↗

PubMed1996

Nonlinear transcranial Doppler analysis demonstrates age-related changes of cerebral hemodynamics.

We studied the age- and gender-related changes in complexity of arterial blood flow by analyzing the maximum velocity waveforms. Data were collected by insonation of the middle cerebral artery blood flow by a 2-MHz transcranial pulsed Doppler system. Following the paradigm of nonlinear dynamical systems or "chaos" theory, complexity is best evaluated by estimating the correlation dimension (D2) and the largest Lyapunov exponent (lambda 1). Forty healthy persons (male/female ratio: 1/1; mean age 48.6 y; range 19-86 y) were studied. No gender-related differences were observed. The age-effect showed a diminishing lambda 1 [df(1, 36) = 5.687; p < 0.022] and an increasing D2 at higher age [df(1, 36) = 4.997; p < 0.032]. The age-related decline of the lambda 1 implies a more prominent periodicity, explained by reduced fluctuations in R-R intervals and an altered gain of the baroreceptor reflex. The increased D2 might be related to more prominent vessel wall oscillations due to the increased vessel wall stiffness at higher age.

Adult↗

PubMed1995

Fractal analysis of the electromyographic interference pattern.

Evaluation of motor unit recruitment is an important component of the clinical EMG exam. Typically this is assessed qualitatively using auditory features and estimates of the visual complexity of the EMG waveform. Recent advances in nonlinear dynamics have led to the development of the concept of fractals which can be used to quantify complexity and space filling features of various structures. This study was undertaken to determine if the normal EMG interference pattern (IP) has fractal characteristics that might be helpful in quantitative analysis. EMG activity was recorded from the 9 normal biceps muscles as force was varied from 10 to 90% of maximal. Using a box count algorithm, the fractal dimension was calculated. The EMG IP displays fractal characteristics with a dimension that is highly correlated with force and ranges from 1.1 to 1.4 as force increases from 10 to 90% MVC. The fractal dimension (FD)-force relationship is similar to that observed with other methods of IP analysis and suggests that the fractal dimension can be used to quantify and capture the essence of the 'complexity' of motor unit recruitment patterns.

Adult↗

PubMed2000

[A quantitative study of poincare dispersed-dot plot for heart rate variability].

Poincare dispersed-dot plot is an important nonlinear dynamics method that can effectively describe the attractor shape. Yet studies in the past generally processed the plot in a graphic mode without further discussion on the quantitative indices. That was not suited for clinical application and studies. So, based on the shape of Poincare dispersed-dot plot, we propose a group of quantitative parameters of Poincare dispersed-dot plot: plot square(SQ), long axis (LA), short axis(SA), and angle related to long-axis and short-axis(ALS). According to the characters of the heart rate Poincare dispersed-dot plot, we have posed the 'tortoise crawling and counting' algorithm to get these four quantitative parameters. We have tested these four quantitative parameters with clinical and animal experiment data. The results demonstrate that these four parameters are of certain specificity to heart diseases, indicating that these parameters might be useful for clinical diagnosis.

Algorithms↗

PubMed2006

Sample entropy of ECG time series of fearful flyers: preliminary results.

Research within the framework of the nonlinear dynamical systems (NDS) in the field of anxiety disorders has shown that greater irregularity/complexity appears in the output from healthy systems. In this study we measured the Heart rate variability (HRV) and the sample sntropy (SampEn) of the ECG mV time series of fearful flyers (N = 15) and a matched control group (N = 15) when confronted with three combinations of feared stimuli (pictures, sounds, and pictures with sounds) as well as relaxing stimuli (pictures and sounds). Fearful flyers had lower SampEn than controls in all conditions, including baseline. Non-phobics showed significant entropy decreases from baseline in two out of three exposure conditions. No differences on HRV were found between groups, and HRV was not sensitive to condition changes. The main finding of the study is that the SampEn calculated on very short ECG mV recordings (10 to 60 seconds, easy to obtain in clinical settings) may be a useful diagnostic measure since it can distinguish fearful from non-fearful flyers.

Adult↗

PubMedDate not supplied

Identification of nonlinear biological systems using Laguerre expansions of kernels.

Identification of nonlinear dynamic systems using the Volterra-Wiener approach requires the estimation of system kernels from input-output data. A kernel estimation technique, originally proposed by Wiener (1958) and recently studied by Ogura (1986), employs Laguerre expansions of the kernels and estimates the unknown expansion coefficients via time-averaging of covariance samples. This paper presents another implementation of the technique which utilizes least-squares fitting instead of covariance time-averaging and provides for the proper selection of the intrinsic Laguerre parameter "alpha". Results from simulation examples demonstrate that this implementation can yield accurate kernel estimates up to 3rd-order from short input-output data records. Furthermore, it is shown that this implementation remains effective in the presence of noise and when the spectral characteristics of the input signal deviate somewhat from the theoretical requirements of whiteness. The computational requirements and the overall performance of this technique compare favorably to existing methods, especially in cases where the system kernels can be represented with a relatively small number of Laguerre basis functions.

Computer Simulation↗

PubMed1997

A new class of biochemical oscillator models based on competitive binding.

It has been noted that single-enzyme systems can undergo strongly damped transient oscillations. In this paper, we present a nonlinear dynamics analysis of oscillations in undriven chemical systems. This analysis allows us to classify transient oscillations into two groups. In the first group, oscillations arise from rapid oscillatory relaxation to a slower transient relaxation mode. These oscillations are always strongly damped. In the second group, it is the slowest relaxation mode which is implicated in the oscillations so these can be very lightly damped. This second class of oscillations has not previously been studied in enzymology. We show that a remarkably simple single-enzyme system, namely competitive inhibition with substrate flow, generates transient oscillations which belong to the second class. In an attempt to design an experimentally realizable version of this model, we then discovered a system which is capable of sustained oscillations. In this experimentally realizable model, two substrates compete to bind to a macromolecule. The flow of one substrate is controlled by a simple feedback device. Sustained oscillations are observed over a very wide range of parameters. In both models, oscillations are favored by a wide disparity in rates of binding and dissociation of the two substrates to the macromolecule.

Binding, Competitive↗

PubMed1994

The assessment of sequential response organization in schizophrenic and control subjects.

1. A novel analysis is introduced for the sequential organization of behavioral elements derived from the ergodic theory of nonlinear dynamical systems and statistical mechanics of physical systems. 2. This analysis yields the fluctuation spectrum of local dynamical entropies, S (h), which quantifies the contributions of subsequences with different degrees of association between elements to the overall observed behavior. In addition, q-dependent order parameter functions assess the relationship between the degree of association between consecutive behavioral elements and qualitative aspects of the subsequences. 3. A binary choice task paradigm is used to extract thought-contingent responses in order to determine the organization of sequences of behavioral actions. 4. A group a schizophrenic patients and controls was tested with a binary choice task paradigm to determine the sequential organization of their responses. 5. The results indicate that the overall response sequences of both schizophrenics and controls are non-random. In addition, clear differences in qualitative aspects between response subsequences with different degrees of association are revealed. Finally, significant fluctuations within individual subjects were found between highly predictable and highly unpredictable response subsequences. 6. These results are discussed with respect to dysregulations in behavioral organization that could not be assessed previously and may provide new insights into the behavioral effects of the underlying dysregulated neural circuitry.

Adult↗

PubMed1997

Spirals, chaos, and new mechanisms of wave propagation.

The chaos theory is based on the idea that phenomena that appear disordered and random may actually be produced by relatively simple deterministic mechanisms. The disordered (aperiodic) activation that characterizes a chaotic motion is reached through one of a few well-defined paths that are characteristic of nonlinear dynamical systems. Our group has been studying VF using computerized mapping techniques. We found that in electrically induced VF, reentrant wavefronts (spiral waves) are present both in the initial tachysystolic stage (resembling VT) and the later tremulous incoordination stage (true VF). The electrophysiological characteristics associated with the transition from VT to VF is compatible with the quasiperiodic route to chaos as described in the Ruelle-Takens theorem. We propose that specific restitution of action potential duration (APD) and conduction velocity properties can cause a spiral wave (the primary oscillator) to develop additional oscillatory modes that lead to spiral meander and breakup. When spiral waves begin to meander and are modulated by other oscillatory processes, the periodic activity is replaced by unstable quasiperiodic oscillation, which then undergoes transition to chaos, signaling the onset of VF. We conclude that VF is a form of deterministic chaos. The development of VF is compatible with quasiperiodic transition to chaos. These results indicate that both the prediction and the control of fibrillation are possible based on the chaos theory and with the advent of chaos control algorithms.

Animals↗

PubMed2006

Calcium instabilities in mammalian cardiomyocyte networks.

The degeneration of a regular heart rhythm into fibrillation (a chaotic or chaos-like sequence) can proceed via several classical routes described by nonlinear dynamics: period-doubling, quasiperiodicity, or intermittency. In this study, we experimentally examine one aspect of cardiac excitation dynamics, the long-term evolution of intracellular calcium signals in cultured cardiomyocyte networks subjected to increasingly faster pacing rates via field stimulation. In this spatially extended system, we observed alternans and higher-order periodicities, extra beats, and skipped beats or blocks. Calcium instabilities evolved nonmonotonically with the prevalence of phase-locking or Wenckebach rhythm, low-frequency magnitude modulations (signature of quasiperiodicity), and switches between patterns with occasional bursts (signature of intermittency), but period-doubling bifurcations were rare. Six ventricular-fibrillation-resembling episodes were pace-induced, for which significantly higher complexity was confirmed by approximate entropy calculations. The progressive destabilization of the heart rhythm by coexistent frequencies, seen in this study, can be related to theoretically predicted competition of control variables (voltage and calcium) at the single-cell level, or to competition of excitation and recovery at the cell network level. Optical maps of the response revealed multiple local spatiotemporal patterns, and the emergence of longer-period global rhythms as a result of wavebreak-induced reentries.

Action Potentials↗

PubMed2000

Self-disturbance as a source of spatiotemporal heterogeneity: the case of the tallgrass prairie.

Tallgrass prairies are characterized by high levels of litter production, which has a profound effect on live biomass. Litter introduces a delayed inhibition of biomass growth, generating nonlinear dynamics and chaos. In this paper, we study a model of biomass-litter interaction, and focus on the litter persistence rate. The observed dynamics depends largely on this rate of year-to-year persistence. Different scenarios are explored and discussed. A spatially extended counterpart of such a model is later on introduced to account for the effects of space. Temporal chaos introduces spatial heterogeneity in terms of gaps where the current year biomass is almost zero. Such gaps can be colonized by fugitive species. The inhibitory effect of litter on biomass is thus an important source of intrinsic, small-scale heterogeneities that may promote diversity. On the other hand, the huge amounts of litter produced by the competitive dominants in tallgrass prairies enhance the probability of fires. Fires benefit, rather than depress, the superior competitive species. This fact explains why the intermediate disturbance hypothesis (IDH) stating that the highest diversity levels should be observed at intermediate disturbance frequencies, does not work in these communities. We define self-disturbances as small-scale disturbances affecting the growth and survival of the individuals that have generated them (e.g. due to the effects of the litter mass they produce). In the absence of other disturbances, self-disturbances can induce high heterogeneity and diversity levels in tallgrass prairies. We discuss the general implications of self-generated disturbances for landscape heterogeneity and diversity of communities in which the main external perturbations benefit the dominant species.

Biomass↗

PubMed2006

Models for the study of whole systems.

This article summarizes a network and complex systems science model for research on whole systems of complementary and alternative medicine (CAM) such as homeopathy and traditional Chinese medicine. The holistic concepts of networks and nonlinear dynamical complex systems are well matched to the global and interactive perspectives of whole systems of CAM, whereas the reductionistic science model is well matched to the isolated local organ, cell, and molecular mechanistic perspectives of pharmaceutically based biomedicine. Whole systems of CAM are not drugs with specific actions. The diagnostic and therapeutic approaches of whole systems of CAM produce effects that involve global and patterned shifts across multiple subsystems of the person as a whole. For homeopathy, several characteristics of complex systems, including the probabilistic nature of attractor patterns, variable sensitivity of complex systems to initial conditions, and emergent behaviors in the evolution of a system in its full environmental context over time, could help account for the mixed basic science and controlled clinical trial research findings, in contrast with the consistently positive outcomes of observational studies in the literature. Application of theories and methods from complex systems and network science can open a new era of advances in understanding factors that lead to good versus poor individual global outcome patterns and to rational triage of patients to one type of care over another. The growing reliance on complex systems thinking and systems biology for cancer research affords a unique opportunity to bridge between the CAM and conventional medical worlds with some common language and conceptual models.

Acupuncture Therapy↗

PubMed2003

Complexity analysis of the temperature curve: new information from body temperature.

An attempt was made to develop a truly quantitative approach to temperature, based on models derived from nonlinear dynamics and chaos theory. Three different procedures for measuring the degree of complexity of the temperature curve were compared, and the possible correlations between these measurements and certain physiopathologically relevant parameters in healthy subjects were examined. Twenty-three healthy subjects (10 males, 13 females) between 18 and 85 years of age had their temperature measured every 10 min for at least 30 h. These time series were used to determine the approximate entropy (ApEn), a detrended fluctuation analysis (DFA), and the fractal dimension by the compass method (FD(c)). There was good correlation between the different methods of measuring the complexity of the curve [ r=-0.603 for ApEn vs. DFA ( p=0.002), r=0.438 for ApEn vs. FDc ( p=0.04) and r=-0.647 for DFA vs. FDc ( p=0.0008)]. Both the fractal dimension and the approximate entropy were inversely correlated with age [ r=-0.637 ( p=0.001) and r=-0.417 ( p=0.03), respectively], while the DFA increased with age ( r=0.413, p=0.04). The results thus suggest that complexity of the temperature curve decreases with age. The complexity of the temperature curve can be quantified in a consistent fashion. Age is associated with lower complexity of the temperature curve.

Adolescent↗