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The majority of research work on triboelectric energy harvesting is on material science, manufacturing and electric circuit design. There is a lack of in-depth research into structural dynamics which is crucial for power generation in triboelectric energy harvesting. In this paper, a novel triboelectric energy harvester with a compact structure working in sliding mode is developed, which is in the form of a casing and an oscillator inside. Unlike most sliding-mode harvesters using single-unit films, the proposed harvester utilizes grating-patterned films which are much more efficient. A bistable mechanism consisting of two pairs of magnets is employed for broadening the frequency bandwidth. A theoretical model is established for the harvester, which couples the structural dynamics domain and electrical dynamics domain. This paper presents the first study about the nonlinear structural dynamics of a triboelectric energy harvester with grating-patterned films, which is also the first triboelectric energy harvester integrating grating-patterned films with a bistable magnetic system for power performance enhancement. Theoretical studies are carried out from the perspectives of both structural and electrical dynamics. Surface charge density and segment configuration of the films affect whether the electrostatic force influences the structural dynamics, which can be neglected under a low surface charge density. Differences in structural response and electrical output are found between a velocity-dependent model and Coulomb’s model for modelling the friction in the triboelectric energy harvesting system. The bistable mechanism can effectively improve the output voltage under low-frequency excitations. Additionally, the output voltage can also be obviously enhanced through increasing the number of the hollowed-out units of the grating-patterned films, which also results in a slight decrease in the optimal load resistance of the harvester. These findings enable innovative designs for triboelectric energy harvesters and provide fabrication guidelines in practical applications.

Mathematical models of the electrical response of cardiac cells are used to help develop an understanding of the electrophysiological properties of cardiac cells. Increasingly complex models are being developed in an effort to enhance the biological fidelity of the models and potentially increase their ability to predict electrical dynamics observed in vivo and in vitro. However, as the models increase in size, they have a tendency to become unstable and are highly sensitive to changes in established parameters. This means that such models might be unable to accurately predict person-to-person variability, dynamical changes due to disease pathologies that alter ionic currents, or the effect of treatment with antiarrhythmics. In this paper, we test the predictive limits of two mathematical models by altering the conductance of Ca2+, Na+, and K+ channels. We assess changes in action potential duration (APD), rate dependence, hysteresis, dynamical behavior, and restitution as conductance is varied. We find model predictions of abrupt changes in measured quantities and differences in the predictions of the two models that might be missed in a less systematic approach. These features can be compared to experimental observations to help assess the fidelity of the models.

This paper presents a novel modeling method for assessing power system oscillatory modes with the aim at further controllers’ design. The method is denominated Electromechanical Energy Approach since it regards the idea of mechanical rotating bars to represent each node of the power system. The main difference between the presented method and others lies on the fact that the Eletromechanical Energy Approach regards intrinsically the voltage dynamics of system's nodes, i.e., both mechanical and electrical dynamics are obtained inherently. Besides, this approach reduces model's complexity and allows a better fitting for system's oscillation modes, both very welcome features whenever one wants an accurate and feasible model for designing power system controllers.

The conflict monitoring model predicting higher anterior cingulate cortex (ACC) neuronal activity on incongruent trials has been recently challenged by a model predicting longer neuronal activity in incongruent trials characterized by longer RTs. To clarify this issue, brain dynamics were explored through event-related-potential (ERP) recordings during a Stroop task. We assessed differences between experimental conditions by combining complementary methods sensitive to the temporality of events including microstate, TANOVA and source localization analysis. The analysis demonstrated the same electrical dynamics only differed in duration towards the end of information processing in the incongruent condition. Specifically, the activation strength of the ACC region did not differ significantly between congruent and incongruent conditions but lasted longer in the incongruent condition. Taken together, our results support the model predicting longer neuronal activity in incongruent trials characterized by longer RTs. They highlight that brain dynamics can dramatically change through periods of interest and that caution is required when interpreting fMRI results. To conclude, these results indicate how time-sensitive measures can contribute to a better understanding of the mechanisms underlying information processing, and thus offer new venues to explore conflict monitoring

The paper presents evaluation of benefits of using a vectorcardiogram (VCG) application in education and clinical practice. In contrast to commonly used visualizations of the ECG signal, spatial presentation of vectorcardiogram is not frequently used, although this approach offers more sensitive and precise insights in cardiac electrical dynamics. In education spatial analysis of cardiac electrical activity is important for understanding the relation between the cardiac abnormalities and the characteristic shapes of the ECG. Technology advancements facilitated VCG related research, including development of novel VCG descriptors, but lack of standardization and established methodology prevented the analyses of VCG to become more popular. The educational version of the VCG application is demonstrated to potential users: medical and engineering students, and their feedback regarding the application benefits for education and clinical practice is collected. The evaluation results are analyzed and activities to raise interest and promote VCG usage are discussed.

The chaotic spatio-temporal electrical activity during life-threatening cardiac arrhythmias like ventricular fibrillation is governed by the dynamics of vortex-like spiral or scroll waves. The organizing centers of these waves are called wave tips (2D) or filaments (3D) and they play a key role in understanding and controlling the complex and chaotic electrical dynamics. Therefore, in many experimental and numerical setups it is required to detect the tips of the observed spiral waves. Most of the currently used methods significantly suffer from the influence of noise and are often adjusted to a specific situation (e.g. a specific numerical cardiac cell model). In this study, we use a specific type of deep neural networks (UNet), for detecting spiral wave tips and show that this approach is robust against the influence of intermediate noise levels. Furthermore, we demonstrate that if the UNet is trained with a pool of numerical cell models, spiral wave tips in unknown cell models can also be detected reliably, suggesting that the UNet can in some sense learn the concept of spiral wave tips in a general way, and thus could also be used in experimental situations in the future (ex-vivo, cell-culture or optogenetic experiments).

Cardiac ventricular arrhythmias - electrical activation abnormalities that can result in sudden cardiac death - are difficult to manage with ablative treatment as many have complex, non-sustained 4D (volume over time) activation patterns that cannot be fully captured by existing electroanatomic mapping systems. In this study, we describe the use of acoustoelectric cardiac imaging (ACI) for high resolution, beat-to-beat mapping of electrical dynamics in an in vivo swine model. Beat-to-beat variability was tracked in the swine; the variation in the peak magnitude, peak myocardial location, peak time, and SNR across 20 beats were measured as 5.27 ± 0.68 µV, 3.32 ± 0.41 mm from the epicardium, -2.73 ± 0.38 ms from the peak EGM, and 25.3 ± 8.1 dB, respectively. Additionally, propagation velocity was tracked across four pigs and was calculated as 0.153 ± 0.108 m/s along the endocardial-epicardial axis and 0.123 ± 0.098 m/s along the apicobasal axis. This study suggests that ACI has the potential to improve ablation treatment of arrhythmias through higher resolution, beat-to-beat pre-ablation mapping and characterization of ventricular arrhythmias.

Many cortical and subcortical regions contribute to complex social behavior; nevertheless, the network level architecture whereby the brain integrates this information to encode appetitive socioemotional behavior remains unknown. Here we measure electrical activity from eight brain regions as mice engage in a social preference assay. We then use machine learning to discover an explainable brain network that encodes the extent to which mice chose to engage another mouse. This socioemotional network is organized by theta oscillations leading from prelimbic cortex and amygdala that converge on ventral tegmental area, and network activity is synchronized with brain-wide cellular firing. The network generalizes, on a mouse-by-mouse basis, to encode socioemotional behaviors in healthy animals, but fails to encode an appetitive socioemotional state in a ‘high confidence’ genetic mouse model of autism. Thus, our findings reveal the architecture whereby the brain integrates spatially distributed activity across timescales to encode an appetitive socioemotional brain state in health and disease.

Cardiac excitation-contraction (E-C) coupling is influenced by (at least) three dynamic systems that couple and feedback to one another (Abstract Figure). Here we review the mechanical effects on cardiomyocytes that include mechano-electro-transduction (commonly referred to as mechano-electric coupling, MEC) and mechano-chemo-transduction (MCT) mechanisms at cell and molecular levels which couple to Ca(2+)-electro and E-C coupling reviewed elsewhere. These feedback loops from muscle contraction and mechano-transduction to the Ca(2+) homeodynamics and to the electrical excitation are essential for understanding the E-C coupling dynamic system and arrhythmogenesis in mechanically loaded hearts. This white paper is comprised of two parts each reflects the key aspects from the 2018 UC Davis symposium: MEC (how mechanical load influences electrical dynamics) and MCT (how mechanical load alters cell signaling and Ca(2+) dynamics). Of course, such separation is artificial since Ca(2+) dynamics profoundly affects ion channels and electrogenic transporters and vice-versa. In time these dynamic systems and their interactions must become fully integrated, and that should be a goal for a comprehensive understanding of how mechanical load influences cell signaling, Ca(2+) homeodynamics and electrical dynamics. In this white paper we will emphasize current understanding, consensus, controversies, and the pressing issues for future investigations. Space constraints make it impossible to cover all relevant articles in the field, so we will focus on the topics discussed in the symposium.

This work proposes a deterministic robust controller to improve tracking performance for a linear motor, taking into account the electrical dynamics imposed by a commercial current controller. The design is split in two parts by means of the backstepping technique, in which the first part corresponds to a typical deterministic robust controller, neglecting the electrical dynamics. In the second part, a second-order electrical dynamics is considered using a particular state transformation. There, the proposed control law is composed of a term to compensate the known part of the model and a robust control term to impose a bound on the effect of uncertainties on tracking error. Stability and boundedness results for the complete controller are given. To this effect, a general result on boundedness and stability of nonlinear systems with conditionally bounded state variables is derived first. Finally, experimental results for the complete controller show an improvement on tracking error of up to 31.7% when compared with the results from the typical controller that neglects the electrical dynamics. Fil: Villegas, Fernando Javier. Universidad Nacional de la Pampa. Facultad de Ingeniería; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina Fil: Hecker, Rogelio Lorenzo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de la Pampa. Facultad de Ingeniería; Argentina Fil: Peña, Miguel. Universidad Nacional de San Juan. Facultad de Ingeniería. Instituto de Automática; Argentina

Computer model coupled blood pressure dynamics in magisterial and small arteries networks and its stabilizing effect on heart functioning has been suggested. The Fermi-Pasta-Ulam auto recurrence in the description of the electrical activity of the heart has demonstrated the universal role of the FPU recurrence in the study of distributed dynamical systems. The heart electrical dynamics was described by the coupled Van der Pol differential equations with a time lag, linked with two additively coupled nonlinear differential equations circumscribing the blood pressure dynamics in the networks of magisterial and small arteries.

International audience; Marine renewable energies (MRE) allow the diversification of the energy mix and are thus developing rapidly. The extraction of tidal energy is one of the most promising MRE and is nowadays in a preindustrial phase. The Alderney Race (Raz Blanchard in French), situated between the island of Alderney (Channel Islands) and the Cap de la Hague in France, capitalizes a resource estimated between 5 and 14 TWh/year. This great tidal resource attracts tidal energy developers. Harnessing the high potential of the Alderney Race requires deploying numerous turbines in arrays. The purpose of this paper is to investigate the influence of the wake effects on the electrical dynamics of commercial tidal farms. The proposed methodology is applied to a hypothetical tidal farm in the Alderney Race. The influence of the wake effects on the power produced by a tidal farm is assessed by comparing simulations where the effect is neglected to simulations with wake effects. Two operational conditions are analyzed: Normal operation (one-day simulation with varying tidal speed); grid disturbances (voltage dip at the tidal farm point of common coupling). Simulations show that neglecting the wake effect implies significant errors in active power estimation at the point of common coupling during normal operation. Moreover, differences in the behavior of the system during voltage dip occur if the wake effect is not taken into account.

Understanding how heterogeneous cells within a multicellular system interact and affect overall function is difficult without a means of perturbing individual cells or subpopulations. Here we apply optogenetics to understand how subpopulations of β-cells control the overall [Ca2+]i response and insulin secretion dynamics of the islets of Langerhans. We spatiotemporally perturbed electrical activity in β-cells of channelrhodopsin2-expressing islets, mapped the [Ca2+]i response, and correlated this with the cellular metabolic activity and an in silico electrophysiology model. We discovered organized regions of metabolic activity across the islet, and these affect the way in which β-cells electrically interact. Specific regions acted as pacemakers by initiating calcium wave propagation. Our findings reveal the functional architecture of the islet, and show how distinct subpopulations of cells can disproportionality affect function. These results also suggest ways in which other neuroendocrine systems can be regulated, and demonstrate how optogenetic tools can discern their functional architecture.

In this study, it was aimed to classify the electrical signals recorded from human brain during different season (summer-winter) videos as stimuli. Data have been recorded using 14 channels EEG from four male participants. The power of delta, theta, alpha, beta and gamma frequency bands have been recorded and used to classify the collected data. Decision tree pre-processing method have been used to select the attributes of frequency bands and electrodes. To classify the data, support vector machines (SVM), linear discriminant analysis (LDA) and logistic regression (LR) machine learning algorithms have been used. It was found that it was separated %82.25 with SVM, %81 with LDA and %80.75 with LR. The results of three algorithms have shown similar scores.

International audience; While the potential groundbreaking role of mathematical modeling in electrophysiology has been demon-strated for therapies like cardiac resynchronization or catheter ablation, its extensive use in clinics is pre-vented by the need of an accurate customized conductivity identification. Data assimilation techniques are,in general, used to identify parameters that cannot be measured directly, especially in patient-specific set-tings. Yet, they may be computationally demanding. This conflicts with the clinical timelines and volumesof patients to analyze. In this paper, we adopt a model reduction technique, developed by F. Chinesta andhis collaborators in the last 15 years, called Proper Generalized Decomposition (PGD), to accelerate the esti-mation of the cardiac conductivities required in the modeling of the cardiac electrical dynamics. Specifically,we resort to the Monodomain Inverse Conductivity Problem (MICP) deeply investigated in the literaturein the last five years. We provide a significant proof of concept that PGD is a breakthrough in solvingthe MICP within reasonable timelines. As PGD relies on the offline/online paradigm and does not needany preliminary knowledge of the high-fidelity solution, we show that the PGD online phase estimates theconductivities in real-time for both two-dimensional and three-dimensional cases, including a patient-specificventricle.

Little is known about how individual cells sense the macroscopic geometry of their tissue environment. Here we explore whether long-range electrical signaling can convey information on tissue geometry to influence electrical dynamics of individual cells. First, we studied an engineered electrically excitable cell line where all voltage-gated channels were well characterized. Cells grown in patterned islands of different shapes showed remarkably diverse firing patterns under otherwise identical conditions, including regular spiking, period-doubling alternans, and arrhythmic firing. A Hodgkin-Huxley numerical model quantitatively reproduced these effects, showing how the macroscopic geometry affected the single-cell electrophysiology via the influence of gap junction-mediated electrical coupling. Qualitatively similar geometry dependent dynamics were experimentally observed in human induced pluripotent stem cell (iPSC)-derived cardiomyocytes. The cardiac results urge caution in translating observations of arrhythmia in vitro to predictions in vivo where the tissue geometry is very different. We present simulation results and scaling arguments which explore how to extrapolate electrophysiological measurements between tissues with different geometries and different gap junction couplings.

The ability of the mercury beating heart (MBH) system to exhibit sustained mechanical and electrochemical activities simultaneously without any external agent (fluctuating or constant), has attracted researchers for decades. The interplay of these activities could mimic the biological phenomena such as a pulsating heart that occurs due to the coupled tissues exhibiting mechanical as well as electrical dynamics. In the present work, we have studied experimentally the dynamics of electrically coupled two and three autonomous MBH systems. A dynamical triangular (heart) shape, in the traditional watch glass geometry, has been chosen for the experiments. It is found that the redox potentials (electrical behavior) of the quasi-identical (due to the inherent heterogeneities in the setup) MBH systems get synchronized at the intermediate coupling strengths whereas coherence in their mechanical activities occur only at large coupling strengths. To the best of our knowledge, this synchronization phenomenon involving two distinct activities (electrical and mechanical) and different coupling thresholds has not been reported, so far. The coherent mechanical activities means the simultaneous occurrence of compressions and expansions in the coupled Hg drops, which are shown using snapshots. In addition to this, the redox time series have also been provided to demonstrate the synchronization in the electrical behavior of MBH systems. Moreover, a mathematical framework considering only electrical and mechanical components of the MBH systems is presented to validate the experimental findings that the strong synchrony in the redox potentials of the MBH systems is a prerequisite for the synchrony in their mechanical activities.

Recent experimental findings have suggested the important role played by blood vessels within the heart in stabilising arrhythmias. However, this link has yet to be explored computationally. In this paper, we develop a computational framework to model fibre orientation around structural inhomogeneities in myocardial tissue based on information obtained from high-resolution histological and MRI images. This framework allows the simulation of cardiac wavefront propagation for a generalised vessel orientation and position within the ventricular wall and transmural fibre architecture around it. We simulate propagation following different stimulation protocols around a transmural and a sub-epicardial vessel, using both bidomain and monodomain representations. We demonstrate the importance of accurately modelling the fibre structure around blood vessels relative to a simplistic transmurally varying fibre orientation model and suggest how this may impact pro-arrhythmic electrical dynamics. © 2009 Springer Berlin Heidelberg.

A longstanding goal in neuroscience is to understand how spatiotemporal patterns of neuronal electrical activity underlie brain function, from sensory representations to decision making. An emerging technology for monitoring electrical dynamics, voltage imaging using genetically encoded voltage indicators (GEVIs), couples the power of genetics with the advantages of light. Here, we review the properties that determine indicator performance and applicability, discussing both recent progress and technical limitations. We then consider GEVI applications, highlighting studies that have already deployed GEVIs for biological discovery. We also examine which classes of biological questions GEVIs are primed to address and which ones are beyond their current capabilities. As GEVIs are further developed, we anticipate that they will become more broadly used by the neuroscience community to eavesdrop on brain activity with unprecedented spatiotemporal resolution.SIGNIFICANCE STATEMENTGenetically encoded voltage indicators are engineered light-emitting protein sensors that typically report neuronal voltage dynamics as changes in brightness. In this review, we systematically discuss the current state of this emerging method, considering both its advantages and limitations for imaging neural activity. We also present recent applications of this technology and discuss what is feasible now and what we anticipate will become possible with future indicator development. This review will inform neuroscientists of recent progress in the field and help potential users critically evaluate the suitability of genetically encoded voltage indicator imaging to answer their specific biological questions.

In this chapter we introduce the fundamental knowledge of systems with hysteresis. The main dynamics and effects are discussed, such as the static (rate-independent) hysteresis effect, the dynamic hysteresis effect, the creep effect, mechanical vibration, electrical dynamics, and the composite model. The properties of systems with hysteresis are presented in detail. Knowledge of the proposed dynamics, effects, and their properties is necessary to investigate systems with hysteresis. Especially, we propose modeling and control approaches for piezoelectric systems. To make the modeling easier to understand, we provide various figures and experimental data.

Manufacturers of commercial thermoelectric modules provide datasheets of the modules including information and graphs about the performance attained at several working conditions. Details about internal parameters are not made available to customers, because in the broad majority of the cases they are not necessary. However, when developing non-standard applications or conducting research projects it is sometimes necessary to make the modules work in different conditions than those shown in the performance curves. This paper shows a methodology to extract thermoelectric internal parameters from the information provided by performance curves, hence allowing scientists to predict the performance of the module at any working condition. The method is based on the basic equations that link thermal and electrical dynamics in which some parameters must be estimated. As a result it is possible to predict the behavior of the modules if they are operated in a non-standard way. One good example is to simulate how a module designed for cooling applications will behave if used as a Seebeck module for power generation. The proposed methodology has been successfully applied to a commercial Peltier module for which the behavior as a thermoelectric generator was simulated and then tested experimentally, attaining very similar results.

[ES] En el presente trabajo se estudia el control PD con compensación deseada de gravedad de robots rígidos. Se introduce un nuevo criterio, menos conservador, para seleccionar las ganancias proporcionales. Se demuestra estabilidad asintótica global cuando se toma en consideración durante el diseño la dinámica eléctrica de los motores de CD con escobillas usados como actuadores. Este resultado no requiere que la dinámica eléctrica de los actuadores sea rápida comparada con la dinámica de la parte mecánica. Se presenta un estudio formal de la técnica de control conocida como control de par la cual es ampliamente utilizada en la práctica industrial., [EN] This paper is concerned with PD control with desired gravity compensation for rigid robots. We introduce a new, less conservative, tuning procedure to select the controller's proportional gains. We prove global asymptotic stability by taking into account the electrical dynamics of brushed DC-motors actuators. Contrary to the common assumption in literature, our result does not require electrical dynamics to be faster than the mechanical dynamics. We present a formal study of the well known practical control strategy called torque control., V. Santibáñez agradece el apoyo económico de DGEST y CONACyT, México. R.V. Carrillo agradece el apoyo económico de CONACyT, México.

We consider how recently developed multi-resolution exploratory graphical models (MR-EGM) may be estimated in a practical real-world situation. A simple cross-validation procedure based on minimising predictive risk is presented as a means to estimate tuning parameters. Through the use of electroencephalography (EEG) data, we attempt to use such a procedure to build a generative (multi-resolution) model of the electrical dynamics in the brain throughout an epileptic seizure. Brain dynamics are analysed by projecting the estimated model parameters onto their principle components where we identify two clusters of seizure activity. To conclude, we discuss the interpretation of such a principle component analysis and how well we can generalise between seizures on a specific patient.

We describe a useful setting for interactive, real-time study of mathematical models of cardiac electrical activity, using implicit and explicit integration schemes implemented in JAVA. These programs are intended as a teaching aid for the study and understanding of general excitable media. Particularly for cardiac cell models and the ionic currents underlying their basic electrical dynamics. Within the programs, excitable media properties such as thresholds and refractoriness and their dependence on parameter values can be analyzed. In addition, the cardiac model applets allow the study of reentrant tachyarrhythmias using premature stimuli and conduction blocks to induce or to terminate reentrant waves of electrical activation in one and two dimensions. The role of some physiological parameters in the transition from tachycardia to fibrillation also can be analyzed by varying the maximum conductances of ion channels associated with a given model in real time during the simulations. These applets are available for download at http://arrhythmia.hofstra.edu or its mirror site http://stardec.ascc.neu.edu/~fenton.

Voltage-sensitive dye imaging (VSDI) can simultaneously monitor the spatiotemporal electrical dynamics of thousands of neurons and is often used to identify functional differences in models of neurological disease. While the chief advantage of VSDI is the ability to record spatiotemporal activity, there are no tools available to visualize and statistically compare activity across the full spatiotemporal range of the VSDI dataset. Investigators commonly analyze only a subset of the data, and a majority of the dataset is routinely excluded from analysis. We have developed a software toolbox that simplifies visual inspection of VSDI data, and permits unaided statistical comparison across spatial and temporal dimensions. First, the three-dimensional VSDI dataset (x,y,time) is geometrically transformed into a two-dimensional spatiotemporal map of activity. Second, statistical comparison between groups is performed using a non-parametric permutation test. The result is a 2D map of all significant differences in both space and time. Here, we used the toolbox to identify functional differences in activity in VSDI data from acute hippocampal slices obtained from epileptic Arx conditional knock-out and control mice. Maps of spatiotemporal activity were produced and analyzed to identify differences in the activity evoked by stimulation of each of two axonal inputs to the hippocampus: the perforant pathway and the temporoammonic pathway. In mutant hippocampal slices, the toolbox identified a widespread decrease in spatiotemporal activity evoked by the temporoammonic pathway. No significant differences were observed in the activity evoked by the perforant pathway. The VSDI toolbox permitted us to visualize and statistically compare activity across the spatiotemporal scope of the VSDI dataset. Sampling error was minimized because the representation of the data is standardized by the toolbox. Statistical comparisons were conducted quickly, across the spatiotemporal scope of the data, without a priori knowledge of the character of the responses or the likely differences between them.

Electrical flicker is a measure of voltage variations which may cause disturbances to consumers. Flicker is caused by both generators and loads connected to the network. This paper discusses the various issues which affect electrical flicker from wind turbines, and describes the development of a software tool capable of predicting, at the design stage, the flicker which would be produced by a wind turbine, or by a wind farm of similar turbines, on a particular network. The paper describes the modelling of the physical dynamics of the wind turbine and the turbulence in the wind which drives it, the electrical dynamics of the generator, and the network itself with various types of embedded consumer loads. Measurements carried out on two different 1 MW wind turbines have been used to validate the models. Copyright © 1998 John Wiley & Sons, Ltd.

Inverse electrocardiography (IECG) estimates cardiac electrical dynamics from body surface electrocardiographic data. As a common practice, all existing IECG problems are solved on anatomically-detailed heart and torso models derived from tomographic images of individual subjects. This practice constitutes a major obstacle to clinical translation of IECG methods, imposing high demands on the quality and processing of medical images. Because anatomical modeling is always associated with variations due to different factors such as image quality and segmentation methods, we design a novel and systematic approach to statistically quantify the impact of ventricular shape variations on the diagnostic accuracy of IECG methods. We propose a novel use of statistical shape modeling to account for the variations in subject-specific anatomical modeling, and from it to generate ventricular models with controlled variations, whose relation to the variations of IECG outputs are then statistically assessed. In this study, we test the feasibility of the proposed approach considering two existing IECG methods for epicardial potential reconstruction and transmural action potential imaging. Both phantom and real-data experiments report statistical equivalency of IECG diagnostic accuracy on ventricular models with local variations. This study demonstrates the feasibility of the proposed approach to be generalized to establish the proper level of anatomical details needed in ventricular modeling, which has the potential to change the common practice and facilitate the clinical translation of IECG research.

Noninvasive Cardiac electrophysiological (EP) imaging aims to compute cardiac electrical dynamics from body surface potential. Anatomical data acquisition and processing computations, to reconstruct detailed geometry of heart and torso, are complex and time consuming tasks that are incompatible with clinical requirements. Our ultimate goal is to improve noninvasive EP imaging techniques toward clinical feasibility by investigating the minimum anatomical information. As the first step toward this goal, in this study we investigate the impact of local geometrical details on cardiac EP imaging. It is known that, global geometrical factors such as size, position and orientation of heart are important in noninvasive electrocardiography problem; but the effect of local geometrical details is unknown and it is difficult to accurately capture. We hypothesize that, as long as global geometrical parameters are captured, local details of realistic cardiac geometry do not significantly impact diagnostic effectiveness of cardiac EP imaging. We verify this hypothesis by developing simple geometrical model instead of realistic heart that enables us to measure local anatomical error, and applying it in EP imaging for detection of myocardial infarction. The results computed based on simple geometrical model are comparable to that of the realistic heart geometry. Thus, it confirms our hypothesis that discarding local geometrical details does not affect diagnostic cardiac EP imaging. The findings of this study pave the road for further studies on tomographic input data processing toward clinical feasibility.

Neuroimaging has become one of the growth areas in the clinical neurological sciences. The advent of CT scanners changed the entire strategy for diagnostic evaluation of patients with neurological disease. This trend was continued with the introduction of MRI scanners, which are now being used to provide information on structural changes too subtle to be appreciated by the unassisted observer. Waiting in the wings as this action has developed over the last two to three decades has been functional brain imaging. It is a current tenet of faith that phenomena of mind can be explained by the chemical and electrical dynamics of the brain. Hence, it has always been assumed that, were techniques only sensitive enough, all neurological disorders (and psychiatric disease) could be described in terms of functional changes in the brain. This notion has been particularly attractive because it suggests that more precise definitions of disease in terms of the underlying functional changes rather than the behavioural phenotypes may be possible. Also there has been the recognition that the behavioural phenotype observed may mask underlying pathological changes, either because it can be influenced by so many interacting factors, or because it lacks specificity. Finally, there has been the increasingly widely held hope that even the adult brain may retain sufficient capacity for adaptive functional reorganization that it can prevent behavioural manifestations of underlying pathology. This brings the promise of new treatment strategies. These considerations have fuelled enthusiasm for functional brain mapping. Functional imaging methods use the electrophysiological, biochemical or physiological changes associated with neuronal activity to map areas of the brain that are involved in cognitive processing and …

Cardiac alternans, a beat-to-beat alternation of cardiac electrical dynamics, and ventricular tachycardia, generally associated with a spiral wave of electrical activity, have been identified as frequent precursors of the life-threatening spatiotemporally chaotic electrical state of ventricular fibrillation (VF). Schemes for the elimination of alternans and the stabilization of spiral waves through the injection of weak external currents have been proposed as methods to prevent VF but have not performed at the level required for clinical implementation. In this paper we propose a control method based on linear-quadratic regulator (LQR) control. Unlike most previously proposed approaches, our method incorporates information from the underlying model to increase efficiency. We use a one-dimensional ringlike geometry, with a single control electrode, to compare the performance of our method with that of two other approaches, quasi-instantaneous suppression of unstable modes (QISUM) and time-delay autosynchronization (TDAS). We find that QISUM fails to suppress alternans due to conduction block. Although both TDAS and LQR succeed in suppressing alternans, LQR is able to suppress the alternans faster and using a much weaker control current. Our results highlight the benefits of a model-based control approach despite its inherent complexity compared with nonmodel-based control such as TDAS.

Two-dimensional in silico models have been constructed for investigating electrical dynamics arising due to pacing rates and tissue discontinuities. Discontinuities (structures) are introduced explicitly through flux discontinuities in the intracellular space. These models use isotropic electrophysiological properties with heterogeneity arising solely from tissue structure. Both simplified and tissue-specific structures can be used together with membrane models with varying restitution properties. Stimuli are delivered at increasing pacing rates, and solution restarts enable the investigation of repeat stimulation at any of the rates. Investigations using these models are providing new insights into mechanisms leading to conduction block and arrhythmogenesis in cardiac tissue.

The open-loop transfer function of a complex Ward-Leonard dynamometer system was derived from an analysis of the behaviour of the individual components. The resulting model, which was in good agreement with the open-loop performance of the system, was used to simulate the behaviour of the closed-loop system; this indicated that the system would be unstable. Classical control theory was used to analyse the performance of a number of stabilizing control strategies, and root loci plots, Bode diagrams and Nichols charts were used to assess the resultant performance. A proportional-plus-integral controller combined with lead compensation was predicted to provide acceptable control. The performance of the system with this controller installed was found to satisfy the design requirements of speed of response, limited overshoot and zero steady-state error.

We have developed a computer model to investigate the electrical dynamics of a heart subjected to defibrillation-level shocks. We obtained the following results from our model: a) For simulations with low defibrillation thresholds (DITS), analysis of isochronal maps produced just after the defibrillation pulse revealed a distinct division of the heart into two volumes of excitable cells completely separated by a wall of refractory cells. Conversely, simulations with high DFTs never displayed this complete functional division of the heart. b) Every successful defibrillationpF) trial has a post-shock isoelectric window (i.e. period of no electrical activation) greater than or equal to 156 msec, compared with less than 82 msec for unsuccessful DF. c) Simulations with two consecutive constant-duration(5msec each), variableamplitude, variable-interval DF pulses were performed.The double pulse DF with the 65 msec interval demonstrated an average reduction in DFT energy of 68%(ave=3.42J, sd=.09J), compared with single pulse DF. We concluded that the lowest DFTs were achieved with an interval of 65 msec between the pulses.

This Focus issue describes a problem in electrical dynamics which has fascinated generations of physiologists. There are today so many views of fibrillation that only the rarest generalization can embrace all of them. Fifty-two prominent investigators collaborate here to present aspects of the problem in these eighteen articles (including this introduction) tailored for readers whose principal expertise lies elsewhere. In "The High One's Lay" (Norse Runes, ca. 800) Odin remarks, "Much too early I came to many places: the beer was not yet ready, or was already drunk em leader " but to this one we come at very nearly the right time in 1998. This introduction attempts to guide newcomers by noting the changed or multiple meanings of novel technical terms while sorting the key facts and ideas into an order that facilitates comparison and contrast with those of a dozen years ago. This Focus issue is authored by some of the foremost innovators of both theory and experiment in this area. By assimilating their presentations the readers of Chaos can become well poised to appreciate and evaluate the definitive evidence expected in the next few years. (c) 1998 American Institute of Physics.

The purpose of this study was to investigate the temporal evolution of ventricular fibrillation (VF) in vivo. Thirty continuous 1 minute recordings were obtained during electrically induced VF from the right and left ventricles in 3 dogs using epicardial arrays containing 5 electrodes. From these 5 unipolar recordings dV/dt, voltage gradient, and transmembrane current (I/sub m/) were determined. Deflections with a -/+ I/sub m/ area of 0 (electrotonic deflections) were not analyzed. Analyses of the nonelectrotonic deflections showed that the electrophysiological parameters dV/dt and voltage gradient change significantly over 10 second intervals during the first minute of VF in vivo. The deflection to deflection interval initially speeds up then slows down as VF continues. >

The author describes a mathematical/computer model of a bundle of cardiac fibers, used to examine the effects of strong electrical shocks on the electrical activity of cardiac tissue. The model incorporates both the complex geometry of a bundle of cardiac fibers as well as the complex electrical dynamics of the cardiac membrane.