Voltage Dynamics Research Articles

Three-dimensional standard electrocardiogram: A first approach based on precordial leads.

Traditional electrocardiography (ECG) is limited to two-dimensional (2D) representations, which restricts its ability to capture the full complexity of cardiac electrical activity. We propose a novel three-dimensional (3D) ECG methodology directly derived from standard 2D recordings, providing enhanced spatial insights without requiring additional hardware modifications. Additionally, we introduce a new formulation, which we term "almost-curvature", to optimize the detection of variations in acute ischemic states, which is one of the leading causes of mortality and exhibits sensitivity issues in diagnosis using traditional ECG methods. To introduce the methodology for developing 3D ECG and evaluate its diagnostic utility in detecting morphological changes associated with acute myocardial ischemia through perimeter, curvature, and almost-curvature metrics. We developed a methodology based on spherical-to-Cartesian coordinate transformations applied to standard ECG data from the PhysioNet database. For validation, we utilized datasets from patients with induced myocardial ischemia. We extracted perimeter, curvature, and almost-curvature metrics from both 3D and 2D ECG and compared them across different ischemic states. From method implementation to clinical validation, we used clustering analyses and statistical tests such as Anderson-Darling, Kolmogorov-Smirnov, Mantel, Shapiro-Wilk, Wilcoxon, and the Permutation test. Statistical analysis revealed a correlation between the plane presenting standard deflections in the 3D ECG and the plane displaying the novel loops generated by our methodology. The almost-curvature metric demonstrated an enhanced capacity to detect variations between ischemic states, surpassing the diagnostic performance of traditional curvature metrics. While 2D analyses showed a reduction in curvature and perimeter during ischemia progression, 3D ECG analysis revealed an increase in these metrics, underscoring its ability to capture morphological changes that may be overlooked by conventional methods. 3D ECG analysis shows potential to enhance the detection of ischemic alterations, offering a more detailed spatial representation of cardiac electrical activity compared to traditional methods. By leveraging spherical-to-Cartesian transformations, our methodology integrates temporal and voltage dynamics into a unified framework, potentially revealing subtle morphological changes associated with ischemia. Although the methodology remains in its early developmental stage and requires further refinement, its promising diagnostic utility suggests it could significantly enhance cardiac diagnostics. Further studies are essential to validate its clinical applicability and address current limitations.

Impact of system parameters on modal analysis of a DFIG open-loop system under weak, moderate, and strong grid conditions

This article presents an open-loop modal analysis of a doubly fed induction generator (DFIG) connected to a single-machine infinite bus (SMIB) test system. The system includes several components: a wind turbine, wind generator, rotor-side converter (RSC), grid-side converter (GSC), and a line network. The dynamics of DFIG currents and voltages are modelled in a synchronously rotating reference frame under open-loop conditions. A small-signal stability (SSS) analysis identifies four distinct hidden system modes. Additionally, the influence of system parameter variations on these modes is investigated. Grid sensitivity analysis is conducted for varying grid strengths, such as weak, moderate, and strong. The system’s behaviour under grid disturbances and wind speed fluctuations is also studied through MATLAB simulations. Finally, the simulation results are validated through the OPAL-RT real-time simulator for practical applications.

A neuronal least-action principle for real-time learning in cortical circuits.

One of the most fundamental laws of physics is the principle of least action. Motivated by its predictive power, we introduce a neuronal least-action principle for cortical processing of sensory streams to produce appropriate behavioral outputs in real time. The principle postulates that the voltage dynamics of cortical pyramidal neurons prospectively minimizes the local somato-dendritic mismatch error within individual neurons. For output neurons, the principle implies minimizing an instantaneous behavioral error. For deep network neurons, it implies the prospective firing to overcome integration delays and correct for possible output errors right in time. The neuron-specific errors are extracted in the apical dendrites of pyramidal neurons through a cortical microcircuit that tries to explain away the feedback from the periphery, and correct the trajectory on the fly. Any motor output is in a moving equilibrium with the sensory input and the motor feedback during the ongoing sensory-motor transform. Online synaptic plasticity reduces the somatodendritic mismatch error within each cortical neuron and performs gradient descent on the output cost at any moment in time. The neuronal least-action principle offers an axiomatic framework to derive local neuronal and synaptic laws for global real-time computation and learning in the brain.

Deep two-photon voltage imaging with adaptive excitation.

Optical imaging of neuronal voltage dynamics is invaluable to studying brain functions. However, high-speed imaging at significant depth is challenging due to the limitations of the short pixel dwell time and the maximum permissible excitation power in tissues. We report high-speed, deep voltage imaging by applying adaptive excitation, which illuminates the regions of interest only. We imaged neuronal voltage activities across two planes down to 500-630 μm depth in the awake mouse brain. Our techniques can be straightforwardly applied to typical two-photon microscopes.

Computationally efficient data‐driven model predictive control for modular multilevel converters

AbstractThe application of model predictive control (MPC) for the control of modular multilevel converters (MMCs) is widely explored because it offers flexibility in integrating multiobjective control and delivers superior dynamic response. Nonetheless, the increase in computational complexity due to the rise in the number of submodules (SMs) is one of the major drawbacks of this technique. This paper presents a finite control set model predictive control (FCS‐MPC) that significantly reduces the computational complexity by employing sparse identification of non‐linear systems (SINDy) to obtain a simplified linear model for the MMC. The SINDy model reduces the complexity of performing the prediction step by integrating input terms into the dynamics of load current and circulating current. This simplifies the implementation compared to the conventional FCS‐MPC approaches by eliminating the need to evaluate the voltage dynamics. The computational burden is further reduced while maintaining voltage levels at the output by restricting the number of combinations for the inserted SMs to only instead of . A detailed comparison between the proposed technique and the existing strategies demonstrates that the proposed technique offers a more computationally efficient solution for implementing FCS‐MPC on MMCs, while improving the circulating current suppression due to more accurate predictions. Simulation and experimental results are presented to validate the performance of the proposed approach.

Common-Mode Frequency of Power Systems Affected by Voltage Dynamics

Common-Mode Frequency of Power Systems Affected by Voltage Dynamics

Resolving spatiotemporal electrical signaling within the islet via CMOS microelectrode arrays.

Glucose-stimulated beta-cells exhibit synchronized calcium dynamics across the islet that recruit beta-cells to enhance insulin secretion. Compared to calcium dynamics, the formation and cell-to-cell propagation of electrical signals within the islet are poorly characterized. To determine factors that influence the propagation of electrical activity across the islet underlying calcium oscillations and beta-cell synchronization, we used high-resolution CMOS multielectrode arrays (MEA) to measure voltage changes associated with the membrane potential of individual cells within intact C57BL6 mouse islets. We measured fast (milliseconds, spikes) and slow (seconds, waves) voltage dynamics. Single spike activity and wave signal velocity were both glucose-dependent, but only spike activity was influenced by NMDA receptor activation or inhibition. A repeated glucose stimulus revealed a highly responsive subset of cells in terms of spike activity. When islets were pretreated for 72 hours with glucolipotoxic medium, the wave velocity was significantly reduced. Network analysis confirmed that in response to glucolipotoxicity the synchrony of islet cells was affected due to slower propagating electrical waves and not due to altered spike activity. In summary, this approach provided novel insight regarding the propagation of electrical activity and the disruption of cell-to-cell communication due to excessive stimulation.

Parameter Identification of Pseudo-Two-Dimensional Model for Lithium-Ion Batteries with Silicon/Graphite Composite Electrodes

In this study, we assess the performance of lithium-ion batteries (LIBs) equipped with silicon/graphite composite electrodes and estimate the electrochemical properties of each constituent material. We employ a composite electrode model integrated into the conventional pseudo-two-dimensional (P2D) model to simulate the electrochemical dynamics of the battery. First, the accuracy of the numerical model is validated by comparing its voltage predictions with existing experimental data. The model successfully captures the voltage characteristics of LIBs across various C-rates. Additionally, the interaction effects between silicon and graphite within the composite electrode are validated by comparing them to previous simulation results. Specifically, the reactivity of silicon and graphite, which varies with the operating voltage range, is accurately assessed under various operating conditions.Parameter estimation for the electrochemical model is performed using a genetic algorithm. Ten parameters significant to the electrochemical performance of each material are selected, considering their physical significance and the feasibility of experimental validation. These parameters are optimized by minimizing a fitness function defined by the relative errors between the experimental and simulated voltage, utilizing 1/3C discharge and pulse data from different states of charge (SOCs). The simulated voltages with the optimized parameters align well with the experimental data, accurately representing both the constant-current discharging voltage and the voltage dynamics during pulse application and removal. This alignment confirms that the estimated parameters effectively characterize the electrochemical behavior of LIBs with the composite electrode.A comparative analysis of parameter identification using only constant-current discharging data versus a combination of constant-current and pulse data reveals significant variations in the distribution of estimated values. Parameters derived solely from constant-current data inadequately fit the pulse data, highlighting the necessity of incorporating diverse data types for reliable parameter estimation.The methodology proposed in this work provides a robust framework for evaluating the electrochemical properties and performance of LIBs featuring silicon/graphite composite electrodes. This approach offers a detailed assessment of the material-level properties by identifying critical electrochemical parameters. Future work will focus on developing an efficient experimental protocol for parameter estimation, employing sensitivity analysis and case studies across different voltage profiles to ensure reliable parameter determination. Figure 1

Modeling Analysis of the Impact of Platinum Oxides and Cerium Ion Migration on PEMFC Performance and Degradation Under a Realistic Driving Cycle

The sluggish oxygen reduction reaction (ORR) kinetics represents the major source of efficiency loss in a Proton Exchange Membrane Fuel Cell (PEMFC) [1]. In the potential range where fuel cells operate, several platinum (Pt) oxide species form on the catalyst surface [2]. Their formation and reduction is a dynamic process which strongly influences ORR activity and the loss of electrocatalyst active surface area (ECSA) [3]. PEMFC performance and durability are also significantly affected by Cerium (Ce) ions which, under typical operating conditions, are known to migrate through the polymer electrolyte membrane (PEM) and the catalyst layers (CLs) driven by concentration and potential gradients [4]. Such mobility reduces scavenging efficacy, thus accelerating PEM degradation. Moreover, it affects cell performance since the progressive build-up, during normal fuel cell operation, of Ce ions into the cathode CL is found to decrease proton conductivity [5] and hinder oxygen diffusion through the ionomer film [4], as highlighted by the most recent literature. In this scenario, a 1+1D multiphase dynamic non isothermal PEMFC model of performance is developed [6]: the close relationship between ORR reaction and Pt oxide dynamics is investigated by modeling oxides formation/reduction with a four-step oxidation mechanism which involves three different types of oxide species. Furthermore, based on the work of [2], the effects of ionomer sulfonate groups adsorption onto platinum surface and of local relative humidity are also included in the ORR kinetics. As visible in Figure 1(a), the proposed ORR formulation allows to properly capture cell voltage dynamics under the operating conditions of the automotive ID-FAST cycle [7]. The PEMFC model of performance also accounts for Ce ions mobility, both in the along-the-channel and through-the-membrane directions: for this purpose, a Nernst-Planck equation is adopted, where the diffusivity and migration coefficients are properly calibrated and validated (Figure 1(b)) by reproducing the Ce ions migration profiles reported in the literature for different current density [5].Lastly, the 1+1D multiphase dynamic non isothermal PEMFC model is coupled with a physical-based 0D model of platinum degradation [3]. The presented model framework is validated on experimental data gathered on a segmented hardware and representative of 600 cycles in the low power conditions of the automotive ID-FAST driving cycle load protocol [7]: as visible in Figure 1(c), the 67% of the initial ECSA is lost. Succeeding in the prediction, this modelling structure aspires to provide critical information for the development of proper mitigation strategies aimed at extending the lifetime of PEMFC stacks.This work received support from the Italian government Progetto PERMANENT - BANDO MITE PNRR Missione 2 Investimento 3.5 A - RSH2A_0O0012.[1] H.A. Gasteiger et al., Activity benchmarks and requirements for Pt, Pt-alloy, and non-Pt oxygen reduction catalysts for PEMFCs, Appl. Catal. B Environ. 56 (2005) 9–35. https://doi.org/10.1016/j.apcatb.2004.06.021.[2] A. Kongkanand et al., Platinum Surface Oxide and Oxygen Reduction Reaction Kinetics during Transient Fuel Cell Operation, J. Electrochem. Soc. 170 (2023) 094506. https://doi.org/10.1149/1945-7111/acfbbb.[3] E. Colombo, Understanding and mitigating degradation of PEMFC caused by real automotive operation, (2023). https://hdl.handle.net/2027.42/155288.[4] A.M. Baker et al., Cerium Ion Mobility and Diffusivity Rates in Perfluorosulfonic Acid Membranes Measured via Hydrogen Pump Operation, J. Electrochem. Soc. 164 (2017) F1272–F1278. https://doi.org/10.1149/2.1221712jes.[5] V.M. Ehlinger et al., Modeling proton-exchange-membrane fuel cell performance/degradation tradeoffs with chemical scavengers, JPhys Energy. 2 (2020). https://doi.org/10.1088/2515-7655/abb194.[6] F. Verducci et al., Dynamic Modeling of Polymer Electrolyte Membrane Fuel Cells Under Real-World Automotive Driving Cycle with Experimental Validation on Segmented Single Cell, Renew. Energy. (Currently under review).[7] E. Colombo et al., PEMFC performance decay during real-world automotive operation: Evincing degradation mechanisms and heterogeneity of ageing, J. Power Sources. 553 (2023) 232246. https://doi.org/10.1016/j.jpowsour.2022.232246. Figure 1

A novel rhodopsin-based voltage indicator for simultaneous two-photon optical recording with GCaMP in vivo.

Genetically encoded voltage indicators (GEVIs) allow optical recording of membrane potential from targeted cells in vivo. However, red GEVIs that are compatible with two-photon microscopy and that can be multiplexed in vivo with green reporters like GCaMP, are currently lacking. To address this gap, we explored diverse rhodopsin proteins as GEVIs and engineered a novel GEVI, 2Photron, based on a rhodopsin from the green algae Klebsormidium nitens. 2Photron, combined with two photon ultrafast local volume excitation (ULoVE), enabled multiplexed readout of spiking and subthreshold voltage simultaneously with GCaMP calcium signals in visual cortical neurons of awake, behaving mice. These recordings revealed the cell-specific relationship of spiking and subthreshold voltage dynamics with GCaMP responses, highlighting the challenges of extracting underlying spike trains from calcium imaging.

Investigation of the Discharge Voltage in Electric Vehicle Motor Bearings

As the automotive industry undergoes a significant transition toward electric vehicles (EV), the matter of electric current discharge in motor bearings has emerged as a focal point of concern. This issue has gathered increased attention due to its potential to inflict damage upon bearing surfaces. The extent of damage inflicted by the discharges is intricately tied to the discharge energy, a factor directly influenced by both bearing capacitance and discharge voltage. In pursuit of a deeper understanding of discharge voltage dynamics within EV motor bearings, electric discharge tests were conducted using in-house modified single ball-on-disk contact and full bearing test equipment. Our test results at the single ball-on-disk contact show that discharge voltage adheres to a probabilistic distribution. Moreover, parallel investigations at the bearing level have yielded corroborative findings, reinforcing conclusions drawn from the single contact tests. These collective efforts contribute to a comprehensive understanding of electric discharge phenomena in motor bearings, essential for developing effective mitigation strategies and advancing the reliability of EV propulsion systems.

Optimal charging of Li-ion batteries using sparse identification of nonlinear dynamics

Optimal charging of Li-ion batteries requires careful management of charge rates, as high rates can lead to accelerated degradation, while low rates significantly extend charging times. Traditional methods for determining charge rates often rely on rule-based approaches, which typically fail to effectively balance battery performance with charging duration. To address this, we introduce a novel optimization approach that directly integrates the dual objectives of minimizing charge time and maximizing battery lifetime into the optimization process. Unlike most existing charge optimization methods that do not directly track battery lifetime and charge time simultaneously, our method employs a data-driven model that facilitates direct and dynamic estimation of both battery lifetime and charge time at each step of the optimization process. Specifically, we utilize the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to predict battery capacity and voltage dynamics, which informs the calculations of lifetime and charge time required to solve the optimization problem. This approach provides a balanced optimization strategy that enhances the effectiveness of battery’s performance while maintaining the efficiency of the charging process. We applied this method to a novel next-generation NMC811 battery, featuring a cathode comprised of 80% nickel, 10% manganese, and 10% cobalt, and a lithium metal foil anode — a combination not extensively studied previously. Experimental validation demonstrated that when optimized charge rates are applied every 10 cycles in a 100-cycle operation, the method leads to more stable cycling and improved capacity retention of approximately 7.4% over the nominal charge rate, demonstrating the potential of the developed approach.

Voltage and Current Dynamics Cancellation of Weak-Grid-Tied Grid-Following Inverters for Maximum Transferable Power Improvement

Voltage and Current Dynamics Cancellation of Weak-Grid-Tied Grid-Following Inverters for Maximum Transferable Power Improvement

Imaging high-frequency voltage dynamics in multiple neuron classes of behaving mammals.

Fluorescent genetically encoded voltage indicators report transmembrane potentials of targeted cell-types. However, voltage-imaging instrumentation has lacked the sensitivity to track spontaneous or evoked high-frequency voltage oscillations in neural populations. Here we describe two complementary TEMPO voltage-sensing technologies that capture neural oscillations up to ~100 Hz. Fiber-optic TEMPO achieves ~10-fold greater sensitivity than prior photometry systems, allows hour-long recordings, and monitors two neuron-classes per fiber-optic probe in freely moving mice. With it, we uncovered cross-frequency-coupled theta- and gamma-range oscillations and characterized excitatory-inhibitory neural dynamics during hippocampal ripples and visual cortical processing. The TEMPO mesoscope images voltage activity in two cell-classes across a ~8-mm-wide field-of-view in head-fixed animals. In awake mice, it revealed sensory-evoked excitatory-inhibitory neural interactions and traveling gamma and 3-7 Hz waves in the visual cortex, and previously unreported propagation directions for hippocampal theta and beta waves. These technologies have widespread applications probing diverse oscillations and neuron-type interactions in healthy and diseased brains.

The Na+/K+-ATPase generically enables deterministic bursting in class I neurons by shearing the spike-onset bifurcation structure.

Slow brain rhythms, for example during slow-wave sleep or pathological conditions like seizures and spreading depolarization, can be accompanied by oscillations in extracellular potassium concentration. Such slow brain rhythms typically have a lower frequency than tonic action-potential firing. They are assumed to arise from network-level mechanisms, involving synaptic interactions and delays, or from intrinsically bursting neurons. Neuronal burst generation is commonly attributed to ion channels with slow kinetics. Here, we explore an alternative mechanism generically available to all neurons with class I excitability. It is based on the interplay of fast-spiking voltage dynamics with a one-dimensional slow dynamics of the extracellular potassium concentration, mediated by the activity of the Na+/K+-ATPase. We use bifurcation analysis of the complete system as well as the slow-fast method to reveal that this coupling suffices to generate a hysteresis loop organized around a bistable region that emerges from a saddle-node loop bifurcation-a common feature of class I excitable neurons. Depending on the strength of the Na+/K+-ATPase, bursts are generated from pump-induced shearing the bifurcation structure, spiking is tonic, or cells are silenced via depolarization block. We suggest that transitions between these dynamics can result from disturbances in extracellular potassium regulation, such as glial malfunction or hypoxia affecting the Na+/K+-ATPase activity. The identified minimal mechanistic model outlining the sodium-potassium pump's generic contribution to burst dynamics can, therefore, contribute to a better mechanistic understanding of pathologies such as epilepsy syndromes and, potentially, inform therapeutic strategies.

Bioelectronic Delivery of Potassium Ions Controls Membrane Voltage and Growth Dynamics in Bacteria Biofilms

AbstractBioelectrical signaling, or bioelectricity, is crucial in regulating cellular behavior in biological systems. This signaling, involving ion fluxes and changes in membrane potential (Vmem), is particularly important in the growth of bacterial biofilm. Current microfluidic-based methods for studying bacterial colonies are limited in achieving spatiotemporal control over ionic fluxes due to constant flow within the system. To address this limitation, we have developed a platform that integrates biofilm colonies with bioelectronic ion pumps that enable delivery of potassium (K+) ions, allowing for controlled manipulation of local potassium concentration. Our study examines the impact of controlled K+ delivery on bacterial biofilm growth patterns and dynamics. We observed significant changes in Vmem and coordination within the biofilms. Furthermore, we show that localized K + delivery is highly effective in controlling biofilm expansion in a spatially targeted manner. These findings offer insights into the mechanisms underlying bacterial signaling and growth, and suggest potential applications in bioengineering, synthetic biology, and regenerative medicine, where precise control over cellular signaling and subsequent tissue growth is required.

Erratum: “Dynamic stability of electric power grids: Tracking the interplay of the network structure, transmission losses, and voltage dynamics” [Chaos 32, 053117 (2022)

Erratum: “Dynamic stability of electric power grids: Tracking the interplay of the network structure, transmission losses, and voltage dynamics” [Chaos 32, 053117 (2022)

A grey-box model with neural ordinary differential equations for the slow voltage dynamics of lithium-ion batteries: Application to single-cell experiments

Lithium-ion batteries exhibit a complex, nonlinear and dynamic voltage behaviour. Modelling their slow dynamics is a challenge due to the multiple potential causes involved. We present here a neural equivalent circuit model for lithium-ion batteries including slow voltage dynamics. The model uses an equivalent circuit with voltage source, series resistor, and diffusion element. The series resistance is parameterized using neural networks. The diffusion element is based on a discretized form of Fickian diffusion, parameterized using a neural network and learnable parameters. It is flexible to represent not only Warburg behaviour, but also resistor-capacitor-type dynamics. Mathematically, the resulting model is given by a differential–algebraic equation system combining ordinary and neural differential equations. Therefore, the model combines concepts of both physical theory (white-box model) and artificial intelligence (black-box model) to a combined framework (grey-box model). We apply this approach to a lithium iron phosphate based lithium-ion battery cell. The experimental voltage behaviour during constant-current cycles as well as the dynamics during pulse tests are well reproduced by the model. Only at very high and very low states of charge the simulation significantly deviates from experiments, which might result from insufficient training data in these regions.

A review of advancements in theoretical simulation of oxygen reduction reaction and oxygen evolution reaction single-atom catalysts

Aqueous electrolytes metal-air batteries have garnered widespread attention owing to their safety, low cost, and non-toxic properties. Yet, their development is hindered by slow reaction efficiency. To address this issue, extensive exploratory work has been conducted by researchers. Among them, single-atom catalysts (SACs) represent a significant research direction and a hot topic for the metal-air battery catalysts. Theoretical calculations have been instrumental in predicting and screening high-activity SACs. Besides, the efforts have been made to investigate the performance of SACs for oxygen reduction reaction (ORR) and oxygen evolution reaction (OER). Herein, we specifically analyze the development and prospects of SACs for ORR/OER. The theoretical research on SACs can be divided into two stages. The first stage involves exploring the mechanism of ORR/OER reactions, also known as ideal condition theoretical simulations. The exploration scope can be categorized into four types: 1) transition metals loaded on different substrates; 2) different transition metals loaded on the same substrate; 3) the impact of the coordination environment on the catalytic efficacy of SACs; and 4) the application of machine learning in the field of catalysts. The second stage involves simulating experimental environments, where researchers begin to consider the effects of voltage, solvation, pH, surface states, and microscopic molecular dynamics on catalysts. This work comprehensively reviews the current status of SACs for ORR/OER and provides suggestions for the development of SACs in the next stage.

In vivo calcium and voltage mapping of the zebrafish heart

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Research Foundation Flanders (FWO) Purpose Inherited cardiac arrhythmias are characterized by a disturbance in the normal electrical functioning of the heart due to a dysfunction of cardiac ion channels, accessory proteins and/or structural components. This complex process is difficult to model in vitro, while in vivo mouse models differ substantially from humans with regards to cardiac electrophysiology. Thus, there is a need for alternative disease models in cardiac arrhythmia research. The zebrafish heart shows remarkable similarity to humans in its electrocardiogram and action potential (AP) morphology. Due to the optical translucency of zebrafish larvae, it is possible visualize the cardiac calcium and voltage dynamics across their entire hearts in vivo by use of genetically encoded calcium and voltage indicators (GECI and GEVI). We combined the fast kinetics of a next generation voltage reporter (Ace2N-mNeon) with a spectrally compatible calcium sensor (R-GECO) to create a sensitive high throughput method for in vivo characterization of the cardiac AP. Methods Ace2N-mNeon and R-GECO were expressed under control of a myocardial specific promoter via the Tol2kit system. The effect of sensor expression on normal physiology was assessed by patch clamp experiments. Fluorescence recordings were obtained with a Leica SP8 DLS microscope and a custom-built light sheet microscope. Our zebrafish sensor line was validated by experiments with quinidine drug exposure, as well as a novel model for long QT syndrome (LQTS) with a knock-in (KI) mutation in the cacna1c gene. Results Sensor expression did not significantly alter cardiac electrophysiology. The tg(myl7:Ace2N-mNeon/R-GECO) line demonstrated alterations in the heart rate, depolarization and repolarization induced by quinidine exposure. We observed a significantly prolonged ventricular repolarization in the LQTS KI line. Three-dimensional optical maps of AP characteristics across the entire zebrafish heart exhibited the physiological characteristics of the zebrafish cardiac electrophysiology, with activation initiated at the atrial venous pole and propagated towards the ventricular outflow tract, prolonged repolarization in the ventricles compared to the atria and slowed depolarization in the atrio-ventricular canal. Conclusion By expressing Ace2N-mNeon and R-GECO in the zebrafish heart, we were able to accurately detect drug- and mutation induced alterations in the cardiac AP, as well as AP characteristics across the entire heart, without compromising normal cardiac electrophysiology. Our transgenic line will provide a promising assay for future studies of cardiac arrhythmia.