Principal Dynamic Mode Analysis Research Articles

Dynamic behavior modelling of the silicone-ethanol actuator based on principal dynamic modes of the Laguerre model

The silicone-ethanol actuator is an emerging artificial muscle gaining attention because of its simple construction/stimulation and high-force generation. Most related researches are focused on structural modification methods, while behavior modeling has received less attention. Although the previous models are highly accurate, they suffer from the lack of ethanol content as an input, highly feedback dependency, and fixed output range. In this research, to address the mentioned concerns, the actuator was made according to the conventional procedure and subjected to stimulation, and displacement/blocked-force recording by special custom hardware. The displacement/blocked-force was not limited to a fix amount in order to evaluate ethanol leakage. To predict displacement/blocked-force, a non-autoregressive model was developed with the applied energy, temperature, and ethanol content as inputs. The Laguerre functions was applied to ensure dynamic matching between input-output. Finally, Principal Dynamic Mode analysis was used to optimize the number of filter banks in the model. The results showed that the proposed model provide a reasonable balance between accuracy, interpretability, and simplicity. Also, we found that the prediction of displacement/blocked-force is mainly influenced by the core-temperature/applied-energy, respectively. The proposed model focuses on the effect of stimulation signal and disregards the structural parameters due to their variety in different applications. By considering the both structural parameters and excitation signal, a more comprehensive model can be developed to predict actuator dynamic behavior.

Dynamic Modeling of the Electromyographic and Masticatory Force Relation Through Adaptive Neuro-Fuzzy Inference System Principal Dynamic Mode Analysis

Introduction: Researchers have employed surface electromyography (EMG) to study the human masticatory system and the relationship between the activity of masticatory muscles and the mechanical features of mastication. This relationship has several applications in food texture analysis, control of prosthetic limbs, rehabilitation, and teleoperated robots. Materials and Methods: In this paper, we proposed a model by combining the concept of fuzzy interface systems and principal dynamic mode analysis (PDM). We hypothesized that the proposed approach would provide nonlinear and dynamic characteristics improving the estimation results compared to those obtained by the classical PDM analysis and still having the benefits of a PDM model including the sparse presentation of the system dynamics. After developing PDM, the nonlinear polynomial function of the PDM model was replaced with adaptive neuro-fuzzy inference system (ANFIS) network architecture. After training, the relevant fuzzy rules were extracted and used for creating the fuzzy block (as the nonlinear function block) and predicting the output signal. The proposed approach was later employed to predict bite force using EMG of the temporalis and masseter muscles. Results: Our proposed method outperformed the classical PDM analysis (in terms of our evaluation criteria) in predicting masticatory force . The inter-subject evaluation of the model performance proved that the model created using the data of one subject could be used for predicting masticatory force in other subjects. Conclusion: The proposed model can be helpful in food analysis to predict masticatory force based on the electrical activity of the masseter and temporalis muscles.

Effect of Shallow and Deep SCUBA Dives on Heart Rate Variability.

Prolonged and high pressure diving may lead to various physiological changes including significant alterations of autonomic nervous system (ANS) activity that may be associated with altered physical performance, decompression sickness, or central nervous system oxygen toxicity. Ideally, researchers could elucidate ANS function before, during, and after dives that are most associated with altered function and adverse outcomes. However, we have a limited understanding of the activities of the ANS especially during deeper prolonged SCUBA diving because there has never been a convenient way to collect physiological data during deep dives. This work is one of the first studies which was able to collect electrocardiogram (ECG) data from SCUBA divers at various depths (33, 66, 99, 150, and 200 ftsw; equivalent to 10.05, 20.10, 30.17, 45.72, and 60.96 m of salt water, respectively) breathing different gas mixtures (air, nitrox and trimix). The aim of this study was to shed light on cardiac ANS behavior during dives, including deep dives. With the aid of dry suits, a Holter monitor that could handle the pressure of a 200 ft. dive, and a novel algorithm that can provide a useful assessment of the ANS from the ECG signal, we investigated the effects of SCUBA dives with different time durations, depths and gas mixtures on the ANS. Principal dynamic mode (PDM) analysis of the ECG, which has been shown to provide accurate separation of the sympathetic and parasympathetic dynamics, was employed to assess the difference of ANS behavior between baseline and diving conditions of varying depths and gas mixtures consisting of air, nitrox and trimix. For all depths and gas mixtures, we found consistent dominance in the parasympathetic activity and a concomitant increase of the parasympathetic dynamics with increasing diving duration and depth. For 33 and 66 ft. dives, we consistently found significant decreases in heart rates (HR) and concomitant increases in parasympathetic activities as estimated via the PDM and root mean square of successive differences (RMSSD) for all time intervals (from the first 5 min to the last 30 min) at the bottom depth when compared to the baseline depth at sea level. The sympathetic dynamics did not change with dive duration or gas mixtures, but at the 150 and 200 ft. dives, we found a significant increase in the sympathetic dynamics in addition to the elevated parasympathetic dynamics when compared to baseline The power spectral density (PSD) measures such as the low frequency (LF), high frequency (HF) and its ratio, and approximate entropy (ApEn) indices were not as consistent when compared to PDM-derived parasympathetic dynamics and RMSSD index.

Principal dynamic mode analysis of neural mass model for the identification of epileptic states.

The detection of epileptic seizures in Electroencephalography (EEG) signals is significant for the diagnosis and treatment of epilepsy. In this paper, in order to obtain characteristics of various epileptiform EEGs that may differentiate different states of epilepsy, the concept of Principal Dynamic Modes (PDMs) was incorporated to an autoregressive model framework. First, the neural mass model was used to simulate the required intracerebral EEG signals of various epileptiform activities. Then, the PDMs estimated from the nonlinear autoregressive Volterra models, as well as the corresponding Associated Nonlinear Functions (ANFs), were used for the modeling of epileptic EEGs. The efficient PDM modeling approach provided physiological interpretation of the system. Results revealed that the ANFs of the 1st and 2nd PDMs for the auto-regressive input exhibited evident differences among different states of epilepsy, where the ANFs of the sustained spikes' activity encountered at seizure onset or during a seizure were the most differentiable from that of the normal state. Therefore, the ANFs may be characteristics for the classification of normal and seizure states in the clinical detection of seizures and thus provide assistance for the diagnosis of epilepsy.

Principal dynamic mode analysis of the Hodgkin-Huxley equations.

We develop an autoregressive model framework based on the concept of Principal Dynamic Modes (PDMs) for the process of action potential (AP) generation in the excitable neuronal membrane described by the Hodgkin-Huxley (H-H) equations. The model's exogenous input is injected current, and whenever the membrane potential output exceeds a specified threshold, it is fed back as a second input. The PDMs are estimated from the previously developed Nonlinear Autoregressive Volterra (NARV) model, and represent an efficient functional basis for Volterra kernel expansion. The PDM-based model admits a modular representation, consisting of the forward and feedback PDM bases as linear filterbanks for the exogenous and autoregressive inputs, respectively, whose outputs are then fed to a static nonlinearity composed of polynomials operating on the PDM outputs and cross-terms of pair-products of PDM outputs. A two-step procedure for model reduction is performed: first, influential subsets of the forward and feedback PDM bases are identified and selected as the reduced PDM bases. Second, the terms of the static nonlinearity are pruned. The first step reduces model complexity from a total of 65 coefficients to 27, while the second further reduces the model coefficients to only eight. It is demonstrated that the performance cost of model reduction in terms of out-of-sample prediction accuracy is minimal. Unlike the full model, the eight coefficient pruned model can be easily visualized to reveal the essential system components, and thus the data-derived PDM model can yield insight into the underlying system structure and function.

Principal Dynamic Mode Analysis of EEG Data for Assisting the Diagnosis of Alzheimer's Disease.

We examine whether modeling of the causal dynamic relationships between frontal and occipital electroencephalogram (EEG) time-series recordings reveal reliable differentiating characteristics of Alzheimer’s patients versus control subjects in a manner that may assist clinical diagnosis of Alzheimer’s disease (AD). The proposed modeling approach utilizes the concept of principal dynamic modes (PDMs) and their associated nonlinear functions (ANF) and hypothesizes that the ANFs of some PDMs for the AD patients will be distinct from their counterparts in control subjects. To this purpose, global PDMs are extracted from 1-min EEG signals of 17 AD patients and 24 control subjects at rest using Volterra models estimated via Laguerre expansions, whereby the O1 or O2 recording is viewed as the input signal and the F3 or F4 recording as the output signal. Subsequent singular value decomposition of the estimated Volterra kernels yields the global PDMs that represent an efficient basis of functions for the representation of the EEG dynamics in all subjects. The respective ANFs are computed for each subject and characterize the specific dynamics of each subject. For comparison, signal features traditionally used in the analysis of EEG signals in AD are computed as benchmark. The results indicate that the ANFs of two specific PDMs, corresponding to the delta–theta and alpha bands, can delineate the two groups well.

The autonomic effects of cardiopulmonary decompression sickness in swine using principal dynamic mode analysis

Methods to predict onset of cardiopulmonary (CP) decompression sickness (DCS) would be of great benefit to clinicians caring for stricken divers. Principal dynamic mode (PDM) analysis of the electrocardiogram has been shown to provide accurate separation of the sympathetic and parasympathetic tone dynamics. Nine swine (Sus scrofa) underwent a 15-h saturation dive at 184 kPa (60 ft. of saltwater) in a hyperbaric chamber followed by dropout decompression, whereas six swine, used as a control, underwent a 15-h saturation dive at 15 kPa (5 ft. of saltwater). Noninvasive electrocardiograms were recorded throughout the experiment and autonomic nervous system dynamics were evaluated by heart rate series analysis using power spectral density (PSD) and PDM methods. We observed a significant increase in the sympathetic and parasympathetic tones using the PDM method on average 20 min before DCS onset following a sudden induction of decompression. Parasympathetic activities remained elevated, but the sympathetic modulation was significantly reduced at onset of cutis and CP DCS signs, as reported by a trained observer. Similar nonsignificant observations occurred during PSD analysis. PDM observations contrast with previous work showing that neurological DCS resulted in a >50% reduction in both sympathetic and parasympathetic tone. Therefore, tracking dynamics of the parasympathetic tones via the PDM method may allow discrimination between CP DCS and neurological DCS, and this significant increase in parasympathetic tone has potential use as a marker for early diagnosis of CP DCS.

Principal dynamic mode analysis of hippocampal neuronal networks

Methods The input-output data required for training and modeling was acquired from acute slices of the rat hippocampus. A seizure-like state was induced by perfusion with low Mg2+ artificial cerebrospinal fluid. Gaussian white-noise (GWN) was applied as input to CA3 pyramidal neuron and the output was measured from the same CA3 pyramidal neuron. PDMs were computed using the Laguerre expansion technique.

Principal dynamic mode analysis of action potential firing in a spider mechanoreceptor

The encoding of mechanical stimuli into action potentials in two types of spider mechanoreceptor neurons is modeled by use of the principal dynamic modes (PDM) methodology. The PDM model is equivalent to the general Wiener-Bose model and consists of a minimum set of linear dynamic filters (PDMs), followed by a multivariate static nonlinearity and a threshold function. The PDMs are obtained by performing eigen-decomposition of a matrix constructed using the first-order and second-order Volterra kernels of the system, which are estimated by means of the Laguerre expansion technique, utilizing measurements of pseudorandom mechanical stimulation (input signal) and the resulting action potentials (output signal). The static nonlinearity, which can be viewed as a measure of the probability of action potential firing as a function of the PDM output values, is computed as the locus of points of the latter that correspond to output action potentials. The performance of the model is assessed by computing receiver operating characteristic (ROC) curves, akin to the ones used in decision theory and quantified by computing the area under the ROC curve. Three PDMs are revealed by the analysis. The first PDM exhibits a high-pass characteristic, illustrating the importance of the velocity of slit displacement in the generation of action potentials at the mechanoreceptor output, while the second and third PDMs exhibit band-pass and low-pass characteristics, respectively. The corresponding three-input nonlinearity exhibits asymmetric behavior with respect to its arguments, suggesting directional dependence of the mechanoreceptor response on the mechanical stimulation and the PDM outputs, in agreement to our findings from a previous study (Ann Biomed Eng 27:391-402, 1999). Differences between the Type A and B neurons are observed in the zeroth-order Volterra kernels (related to the average firing), as well as in the magnitudes of the second and third PDMs that perform band-pass and low-pass processing of the input signal, respectively.

Principal dynamic mode analysis of nonlinear transduction in a spider mechanoreceptor.

The nonlinear dynamics of the mechanoelectrical transduction in an arthropod mechanoreceptor (cuticular slit sense organ of the spider Cupiennius salei) were studied using Volterra kernel measurements and the recently proposed method of principal dynamic modes. Since mechanoreceptors must operate with sufficient gain sensitivity to rapidly varying displacement stimuli over a broad bandwidth and for a wide range of amplitudes, the experimental data were generated by applying pseudorandom broadband mechanical displacements of various mean levels to the cuticular slits. The recorded response data were intracellular current and potential. The purpose of this paper is to demonstrate the use of the principal dynamic mode (PDM) methodology in elucidating the nonlinear dynamics of a spider mechanoreceptor. The results obtained demonstrate that two PDMs suffice to provide a complete nonlinear dynamic model of this insect mechanoreceptor. The first PDM resembles the first-order kernel and has a low pass characteristic (position dependent), while the second PDM has a high-pass characteristic (velocity-dependent) and resides entirely in the second-order kernel (nonlinear adaptation). This study may serve as an example of the proper use of this new methodology for the analysis of nonlinear physiological systems.