Cardiac Electrical Signals Research Articles

A CNT-PDMS wearable device for simultaneous measurement of wrist pulse pressure and cardiac electrical activity.

Simultaneous measurement of multi-physiological signals can provide effective diagnosis and therapeutic assessment of diseases. This paper reports a carbon nanotube (CNT) - Polydimethylsiloxane (PDMS) - based wearable device with piezo-resistive and voltage-sensing capabilities for simultaneously capturing wrist pulse pressure and cardiac electrical signal. The layout design of sensing elements in the device was guided by analyzing strain distribution and electric field distribution for minimizing the interference between wrist pulse and cardiac electric activity during measurement. Each device was preconditioned under the strain of 20% until the resistance change of the device reached equilibrium. After preconditioning, the relationship between the resistance change and the pressure was calibrated, which determined the device sensitivity to be 0.01Pa-1 and the linear pressure range of the device to be 0.4kPa to 14.0kPa. Mechanisms of CNT-PDMS for sensing strain signal and electrical pulse signal were explored by scanning electron microscopy (SEM) imaging and equivalent circuit modeling. The device was applied to monitor the wrist pulse and ECG signals of volunteers during the recovering process after physical exercises.

A portable electrocardiogram for real-time monitoring of cardiac signals

This study presents an electrocardiogram (ECG) monitoring and processing system which can observe subjects in real time and display the resultant ECG signals on a computer for observation. The primary application is for the remote observation of cardiac patients. This paper aims to determine its reliability by analysing its portability and wireless connectivity. The system is comprised of three principal units, namely the data acquisition circuit, where cardiac electrical signals are detected using three surface electrodes placed at three different positions on the chest wall to follow the Einthoven Triangle. The signals measured are amplified and filtered by components in a circuit and are then carried to a data processing unit where a ATmega328P microcontroller with a ZigBee interface module are used to transfer the biosignal wirelessly to the Graphical User Interface (GUI) unit which has the capacity to observe ECG biosignals on a computer. The results demonstrated that the design successfully produced a distortion-free signal, namely the hardware and software elements operated and intercommunicated correctly. In both LabVIEW and MATLAB configurations, the GUI characteristics were examined and found to yield unproblematic, user-friendly displays in real-time. Thus, this research provides a novel ECG system design to effectively analyse cardiac patients, however, it would be useful to develop a tool that can differentiate the various forms of cardiac arrhythmia.

ADAPTIVE FILTERING AND ARTIFICIAL INTELLIGENCE METHODS ON FETAL ECG EXTRACTION

Above 30 percent of infant’s death occur due to heart problem like congenital heart disease during 2004 in United States of America. Every year, one in 125 infants is born with heart imperfection. To address these problems, early identification of cardiac anomalies and consistent monitoring of fetal heart can support Pediatric Cardiologist and Obstetrics to take necessary care on time to prescribe medicines and take precautionary measures during gestation period, delivery and/or after birth. Majority of cardiac abnormalities contain some symptoms in the cardiac electrical signal morphology. Electrocardiography gives more information in measuring cardiac signals compare to sonographic measurement. However, in non-invasive heartbeat recording by fetal Electrocardiogram (ECG) application Electrocardiography has its limitation due to low signal- noise ratio where impeding bio-signals are too stronger than fetal electrocardiogram signals. Various adaptive filtering and Artificial intelligence techniques are applied to solve this complex problem. The complex real world problems need a combination of knowledge, skills, and techniques from various sources as an intelligent system. That intelligent system should possess expertise of human, adjust itself to changing environment and learn to improve on its own.

The Feasibility of Arrhythmias Detection from A Capacitive ECG Measurement Using Convolutional Neural Network.

Capacitive ECG (cECG) can measure the cardiac electrical signal via capacitive coupling between electrodes and skin. This unconstrained measurement is suitable for personal heart monitoring; however, the instability in the quality of the signal hinders a further use of the signal. To use the cECG for heart monitoring, an adapted framework that could automatically classify the signal into clear cECG, arrhythmias and noise signal is a prerequisite. In view of this problem, the conventional quality estimation method using predefined features based on R-peak detection is not suitable for this unconstrained measurement of cECG. In this study, we examine the feasibility of arrhythmias detection from the cECG measurement using a convolutional neural network (CNN) model. The malignant ventricular tachycardia (VT) and ventricular fibrillation (VF) do not have the Q-R-S waveforms and therefore may be easily classified as the noise. Hence, in this study, we used the cECG signals that have 3 classes in quality (C1: clear signal; C2: blurry signal with significant R peak and N: noise) and the arrhythmias signals (VT, VF, and atrial fibrillation) from open databases to train the classification model. 13 subjects were recruited in an experiment for the cECG data collection in the Nara Institute of Science and Technology. As a result, the CNN model could recognize C1 and AF signal with over 0.98 recalls and precisions; whereas the recall and precision of VT and VF are lower scores and the lower scores were caused mainly by the similarity between VT and VF. Given the results of the CNN model, this CNN-based framework can accurately label the C1, AF, and malignant ventricular arrhythmias (VT and VF) signals. Further stratification of the C2, VT, and VF will further enhance the use of the cECG measurement.

NEW METHOD EXPLOITING A HYBRID TECHNIQUES FOR FETAL CARDIAC SIGNAL EXTRACTION

According to WHO, 2.6 million babies die during pregnancy. Good monitoring during the prenatal period could provide a significant reduction of this mortality rate. This is possible by detection and extraction of the fetal electrocardiogram (FECG). Extraction of that information is complex due to other noise coming from the mother and within the fetus that drowns out the fetal heart signal. However, new technology and improved filtering technique have provided ways to more accurately and efficiently gather various electrical components regarding fetal heart condition. In this paper, we propose a new source separation filtering method exploiting linear and nonlinear filtering techniques. Our method is a non-invasive extraction technique, where the source signal is the cardiac electrical signal acquired by non-invasive electrodes to facilitate the collection of signals and reduce the cost of the acquisition system; it differs from other existing methods in minimizing the number of input signals and the simplicity of its implementation. The fetal heart signal is drowned out by the maternal electrocardiogram (MECG). The problem that arises is the exact knowledge of the MECG signal affecting the chosen measuring electrode, since the MECG is dependent on the position of the electrode and the type of tissue that goes through. Therefore, its knowledge can be made only by a mathematical estimation. A DWT decomposition with adaptive thresholding based on an LMS filter is applied to extract the fetal signal. So first we extract the QRS complex of the FECG and detect the fetal heart rate (FHR).

Reduced hybrid/complex N-glycosylation disrupts cardiac electrical signaling and calcium handling in a model of dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is the third most common cause of heart failure, with ~70% of DCM cases considered idiopathic. We showed recently, through genetic ablation of the MGAT1 gene, which encodes an essential glycosyltransferase (GlcNAcT1), that prevention of cardiomyocyte hybrid/complex N-glycosylation was sufficient to cause DCM that led to heart failure and early death. Our findings are consistent with increasing evidence suggesting a link between aberrant glycosylation and heart diseases of acquired and congenital etiologies. However, the mechanisms by which changes in glycosylation contribute to disease onset and progression remain largely unknown. Activity and gating of voltage-gated Na+ and K+ channels (Nav and Kv respectively) play pivotal roles in the initiation, shaping and conduction of cardiomyocyte action potentials (APs) and aberrant channel activity was shown to contribute to cardiac disease. We and others showed that glycosylation can impact Nav and Kv function; therefore, here, we investigated the effects of reduced cardiomyocyte hybrid/complex N-glycosylation on channel activity to investigate whether chronic aberrant channel function can contribute to DCM. Ventricular cardiomyocytes from MGAT1 deficient (MGAT1KO) mice display prolonged APs and pacing-induced aberrant early re-activation that can be attributed to, at least in part, a significant reduction in Kv expression and activity that worsens over time suggesting heart disease-related remodeling. MGAT1KO Nav demonstrate no change in expression or maximal conductance but show depolarizing shifts in voltage-dependent gating. Together, the changes in MGAT1KO Nav and Kv function likely contribute to observed anomalous electrocardiograms and Ca2+ handling. These findings provide insight into mechanisms by which altered glycosylation contributes to DCM through changes in Nav and Kv activity that impact conduction, Ca2+ handling and contraction. The MGAT1KO can also serve as a useful model to study the effects of aberrant electrical signaling on cardiac function and the remodeling events that can occur with heart disease progression.

Localized modulated wave solution of diffusive FitzHugh–Nagumo cardiac networks under magnetic flow effect

Motivated by often non-observance clinical effects attributed to some pathological heart diseases, we obtain in this paper the analytical solutions describing localized nonlinear excitations in an improved diffusive FitzHugh–Nagumo cardiac tissue model. The improved model includes the magnetic flux variable used to describe the effect of electromagnetic induction created by fluctuation in ionic concentration during the period when signals are initiated and propagated in the heart. To be consistent with physical units, memristor is used to achieve coupling between membrane potential and magnetic flux such that the induced current from electromagnetic induction is approached. Using the specific reductive perturbation approach in the semi-discrete approximation limit, we show that the whole system dynamics is governed by a modified complex Ginzburg–Landau equation, whose coefficients are dependent on memristive feedback gain. This suggests from biophysical point of view that cardiac electrical signals or waves initiated from the heart sinus node propagates in cardiac networks both in temporal and spatial dimensions in the form of a localized modulated solitonic wave thereby regulating heartbeat as powerful pacemaker. Our analytical solutions are then verified through numerical experiments.

Generalized polynomial chaos-based uncertainty quantification and propagation in multi-scale modeling of cardiac electrophysiology

Uncertainty and physiological variability are ubiquitous in cardiac electrical signaling. It is important to address the uncertainty and variability in cardiac modeling to provide reliable and realistic predictions of heart function, thus ensuring trustworthy computer-aided medical decision-making and treatment planning. Statistical techniques such as Monte Carlo (MC) simulations have been applied to uncertainty quantification and propagation in cardiac modeling. However, MC simulation-based methods are computationally prohibitive for complex cardiac models with a great number of parameters and governing equations. In this paper, we propose to use the Generalized Polynomial Chaos (gPC) expansion in combination with Galerkin projection to analytically quantify parametric uncertainty in ion channel models of mouse ventricular cell, and further propagate the uncertainty across different organizational levels of cell and tissue. To identify the most significant parametric uncertainty in cardiac ion channel and cell models, variance decomposition-based sensitivity analysis was first performed. Following this, gPC was integrated with deterministic cardiac models to propagate uncertainty through ion current, ventricular cell, 1D cable, and 2D tissue to account for the stochasticity and cell-to-cell variability. As compared to MC, the gPC in this work shows the superior performance in terms of computational efficiency. In addition, the gPC models can provide a measure of confidence in model predictions, which can improve the reliability of computer simulations of cardiac electrophysiology for clinical applications.

Heart rate monitoring and therapeutic devices: A wavelet transform based approach for the modeling and classification of congestive heart failure

Heart rate monitoring and therapeutic devices include real-time sensing capabilities reflecting the state of the heart. Current circuitry can be interpreted as a cardiac electrical signal compression algorithm representing the time signal information into a single event description of the cardiac activity. It is observed that some detection techniques developed for ECG signal detection like artificial neural network, genetic algorithm, Hilbert transform, hidden Markov model are some sophisticated algorithms which provide suitable results but their implementation on a silicon chip is very complicated. Due to less complexity and high performance, wavelet transform based approaches are widely used. In this paper, after a thorough analysis of various wavelet transforms, it is found that Biorthogonal wavelet transform is best suited to detect ECG signal's QRS complex. The main steps involved in ECG detection process consist of de-noising and locating different ECG peaks using adaptive slope prediction thresholding. Furthermore, the significant challenges involved in the wireless transmission of ECG data are data conversion and power consumption. As medical regulatory boards demand a lossless compression technique, lossless compression technique with a high bit compression ratio is highly required. Furthermore, in this work, LZMA based ECG data compression technique is proposed. The proposed methodology achieves the highest signal to noise ratio, and lowest root mean square error. Also, the proposed ECG detection technique is capable of distinguishing accurately between healthy, myocardial infarction, congestive heart failure and coronary artery disease patients with a detection accuracy, sensitivity, specificity, and error of 99.92%, 99.94%, 99.92% and 0.0013, respectively. The use of LZMA data compression of ECG data achieves a high compression ratio of 18.84. The advantages and effectiveness of the proposed algorithm are verified by comparing with the existing methods.

In-Silico Modeling of the Functional Role of Reduced Sialylation in Sodium and Potassium Channel Gating of Mouse Ventricular Myocytes.

Cardiac ion channels are highly glycosylated membrane proteins with up to 30% of the protein's mass containing glycans. Heart diseases often accompany individuals with congenital disorders of glycosylation (CDG). However, cardiac dysfunction among CDG patients is not yet fully understood. There is an urgent need to study how aberrant glycosylation impacts cardiac electrical signaling. Our previous works reported that congenitally reduced sialylation achieved through deletion of the sialyltransferase gene, ST3Gal4, leads to altered gating of voltage-gated Na+ and K+ channels ( and , respectively). However, linking the impact of reduced sialylation on ion channel gating to the action potential (AP) is difficult without performing computer experiments. Also, decomposing the sum of K+ currents is difficult because of complex structures and components of channels (e.g., , and ). In this study, we developed in-silico models to describe the functional role of reduced sialylation in both and gating and the AP using in vitro experimental data. Modeling results showed that reduced sialylation changes gating as follows: 1) The steady-state activation voltages of isoforms are shifted to a more depolarized potential. 2) Aberrant K+ currents ( and ) contribute to a prolonged AP duration, and altered Na+ current ( ) contributes to a shortened AP refractory period. This study contributes to a better understanding of the functional role of reduced sialylation in cardiac dysfunction that shows strong potential to provide new pharmaceutical targets for the treatment of CDG-related heart diseases.

Propagation of parametric uncertainty for the K+ channel model in mouse ventricular myocytes.

Cardiac potassium (K+) channel plays an important role in cardiac electrical signaling. Mathematical models have been widely used to investigate the effects of K+ channels on cardiac functions. However, the model of K+ channel involves parametric uncertainties, which can be induced by fitting the model's parameters that best capture experimental data. Since the prediction of cardiac functions are highly parameter-dependent, it is critical to quantify the influence of parametric uncertainty on the model responses to provide the more reliable predictions. This paper presents a new method to efficiently propagate the uncertainty on the model's parameters of K+ channel to the gating variables as well as the current density. In this way, we can estimate the model predictions and their corresponding confidence intervals simultaneously. A generalized polynomial chaos (gPC) expansion approximating the parametric uncertainty is used in combination with the physical models to quantify and propagate the parametric uncertainties onto the modeled predictions of steady state activation and steady state inactivation of the K+ channel. Using Galerkin projection, the variation (i.e., confidence interval) of the gating variables resulting from the uncertainty of model parameters can then be estimated in a computationally efficient fashion. As compared with the Monte Carlo (MC) simulations, the proposed methodology shows it's advantageous in terms of computational efficiency and accuracy, thus demonstrating the potential for dealing with more complicated cardiac models.

Congenital Reductions in Cardiomyocyte N‐glycosylation Alter Voltage‐gated Ion Channel Glycosylation and Function Leading to Compromised Electromechanical Signaling

The regulated process of glycosylation in the heart directly impacts cardiac electrical signaling by altering specific voltage‐gated ion channel gating mechanisms, a phenomenon typically ascribed to the effect of negatively charged sialic acids. In an effort to determine more directly the impact of aberrant N‐glycosylation on cardiomyocyte and cardiac electrical signaling, we created a mouse strain in which the glycosyltransferase responsible for initiating the formation of complex and hybrid N‐glycans, Mgat1, was deleted in cardiomyocytes only (Mgat1KO). Mgat1KO animals show significant cardiac arrhythmias at all ages tested, likely caused by the truncation of voltage‐gated Na+, Ca2+ and K+ channel (Nav, Cav and Kv respectively) N‐glycans that directly impact channel activity. Consistent with studies specifically focused on the impact of sialic acids, Mgat1KO myocyte voltage‐gated ion channels demonstrate depolarizing shifts in gating but to a larger degree than when sialylation only was reduced. In addition to the impact on channel gating, Kv channel current density is significantly reduced in Mgat1KO myocytes compared to WT, suggesting that the lack of complex and hybrid N‐glycans dramatically limits Kv channel activity. Kv channel inactivation kinetics, which are inherent properties of specific Kv isoforms, are also different in Mgat1KO myocytes compared to controls, suggesting significant Kv channel remodeling. Collectively, as a result of such altered channel activity, Mgat1KO myocyte action potential waveforms are significantly affected where a slower activation and marked prolongation of duration are observed. Mgat1KO myocytes also often demonstrate early afterdepolarizations, which are consistent with ECG measurements of Mgat1KO mice showing significant arrhythmias and altered conduction. To test whether the altered myocyte and cardiac electrical activity caused by deletion of Mgat1 would impact electro‐mechanical coupling, we next investigated myocyte intracellular Ca2+ signaling. Mgat1KO myocytes demonstrated a significant increase (~2.5 fold) in spontaneous Ca2+ spark activity compared to WT myocytes. The studies described here illustrate the vital nature of complete N‐linked glycosylation on cardiac function in that reduced levels of cardiomyocyte complex and hybrid N‐glycans lead to significant effects on cardiomyocyte electrical (e.g., ion channel activity) and mechanical (e.g., Ca2+ spark activity) activity in a manner consistent with severe human cardiomyopathies including many forms of congenital disorders of glycosylation.Support or Funding InformationThis work was supported, in part by two grants from the National Science Foundation [IOS‐1146882 and CMMI‐1266331]; an American Heart Association, Greater Southeast Affiliate Grant‐In‐Aid [14GRNT20450148]; an American Heart Association, Greater Southeast Affiliate Postdoctoral Fellowship [15POST25710010]; and a grant from the National Heart, Lung, Blood Institute [1R01HL102171].

The Lack of Cardiomyocyte Complex and Hybrid N‐glycans Result in Arrhythmic Dilated Cardiomyopathy, Heart Failure, and Premature Death

The cardiac glycome, which is the complete set of glycan structures in the heart, is regulated through developmental and cell‐type specific mechanisms. We have shown that this regulation as well as an altered glycome can significantly impact cardiac electrical signaling. To determine more directly the impact of reduced N‐glycosylation on cardiac function, in vivo, we created a mouse strain in which the glycosyltransferase responsible for initiating the formation of complex and hybrid N‐glycans, Mgat1, was deleted in cardiomyocytes only (Mgat1KO). Mgat1KO mice have a significantly higher mortality rate, with all male Mgat1KO mice dying by 44 weeks of age (median age at death was 38 weeks, n=8). Mgat1KO mice present with significantly reduced ejection volumes by six weeks of age that deteriorates rapidly into heart failure between 16 and 30 weeks of age. Further, at an early age, dramatic cardiac arrhythmias present that are consistent with the observed significant effects on voltage‐gated ion channel glycosylation, gating, and functional expression. Our preliminary glycoproteomic and biochemical data show that specific voltage‐gated ion channel isoforms expressed in the Mgat1KO do not have complex and hybrid N‐glycans attached as demonstrated by lectin precipitation/mass spectrometry and protein purification followed by glycan detection through hydrophilic interaction liquid chromatography, respectively. These data support the functional changes in ion channel activity observed, providing evidence of a direct effect of Mgat1 gene deletion on the N‐glycan structures of specific ion channel proteins. The arrhythmias worsen with age, likely caused by the later onset of dilated cardiomyopathy that rapidly declines into arrhythmic heart failure and premature death. The observed altered activity of the less N‐glycosylated ion channels and arrhythmias are consistent with congenital forms of arrhythmic dilated cardiomyopathy and heart failure. Together, our data indicate that complete complex and hybrid N‐glycosylation are necessary for normal cardiac function. Further, the data suggest a link between aberrant glycosylation, chronic arrhythmia, and idiopathic dilated cardiomyopathy. That is, a reduction in N‐glycosylation of specific ion channel subunits causes a significant increase in cardiac arrhythmias and aberrant cytosolic Ca2+ handling that may contribute to the later onset of a thinner, enlarged left ventricle and reduced systolic function diagnosed as idiopathic arrhythmic dilated cardiomyopathy that leads to heart failure and increased mortality.Support or Funding InformationThis work was supported, in part by two grants from the National Science Foundation [IOS‐1146882 and CMMI‐1266331]; an American Heart Association, Greater Southeast Affiliate Grant‐In‐Aid [14GRNT20450148]; an American Heart Association, Greater Southeast Affiliate Postdoctoral Fellowship [15POST25710010]; and a grant from the National Heart, Lung, Blood Institute [1R01HL102171].

Voltage-Sensitive Fluorescence of Indocyanine Green in the Heart

So far, the optical mapping of cardiac electrical signals using voltage-sensitive fluorescent dyes has only been performed in experimental studies because these dyes are not yet approved for clinical use. It was recently reported that the well-known and widely used fluorescent dye indocyanine green (ICG), which has FDA approval, exhibits voltage sensitivity in various tissues, thus raising hopes that electrical activity could be optically mapped in the clinic. The aim of this study was to explore the possibility of using ICG to monitor cardiac electrical activity. Optical mapping experiments were performed on Langendorff rabbit hearts stained with ICG and perfused with electromechanical uncouplers. The residual contraction force and electrical action potentials were recorded simultaneously. Our research confirms that ICG is a voltage-sensitive dye with a dual-component (fast and slow) response to membrane potential changes. The fast component of the optical signal (OS) can have opposite polarities in different parts of the fluorescence spectrum. In contrast, the polarity of the slow component remains the same throughout the entire spectrum. Separating the OS into these components revealed two different voltage-sensitivity mechanisms for ICG. The fast component of the OS appears to be electrochromic in nature, whereas the slow component may arise from the redistribution of the dye molecules within or around the membrane. Both components quite accurately track the time of electrical signal propagation, but only the fast component is suitable for estimating the shape and duration of action potentials. Because ICG has voltage-sensitive properties in the entire heart, we suggest that it can be used to monitor cardiac electrical behavior in the clinic.

Complexity of cardiac signals for predicting changes in alpha-waves after stress in patients undergoing cardiac catheterization

The hierarchical interaction between electrical signals of the brain and heart is not fully understood. We hypothesized that the complexity of cardiac electrical activity can be used to predict changes in encephalic electricity after stress. Most methods for analyzing the interaction between the heart rate variability (HRV) and electroencephalography (EEG) require a computation-intensive mathematical model. To overcome these limitations and increase the predictive accuracy of human relaxing states, we developed a method to test our hypothesis. In addition to routine linear analysis, multiscale entropy and detrended fluctuation analysis of the HRV were used to quantify nonstationary and nonlinear dynamic changes in the heart rate time series. Short-time Fourier transform was applied to quantify the power of EEG. The clinical, HRV, and EEG parameters of postcatheterization EEG alpha waves were analyzed using change-score analysis and generalized additive models. In conclusion, the complexity of cardiac electrical signals can be used to predict EEG changes after stress.

Statistical Metamodeling and Sequential Design of Computer Experiments to Model Glyco-Altered Gating of Sodium Channels in Cardiac Myocytes.

Glycan structures account for up to 35% of the mass of cardiac sodium ( Nav ) channels. To question whether and how reduced sialylation affects Nav activity and cardiac electrical signaling, we conducted a series of in vitro experiments on ventricular apex myocytes under two different glycosylation conditions, reduced protein sialylation (ST3Gal4(-/-)) and full glycosylation (control). Although aberrant electrical signaling is observed in reduced sialylation, realizing a better understanding of mechanistic details of pathological variations in INa and AP is difficult without performing in silico studies. However, computer model of Nav channels and cardiac myocytes involves greater levels of complexity, e.g., high-dimensional parameter space, nonlinear and nonconvex equations. Traditional linear and nonlinear optimization methods have encountered many difficulties for model calibration. This paper presents a new statistical metamodeling approach for efficient computer experiments and optimization of Nav models. First, we utilize a fractional factorial design to identify control variables from the large set of model parameters, thereby reducing the dimensionality of parametric space. Further, we develop the Gaussian process model as a surrogate of expensive and time-consuming computer models and then identify the next best design point that yields the maximal probability of improvement. This process iterates until convergence, and the performance is evaluated and validated with real-world experimental data. Experimental results show the proposed algorithm achieves superior performance in modeling the kinetics of Nav channels under a variety of glycosylation conditions. As a result, in silico models provide a better understanding of glyco-altered mechanistic details in state transitions and distributions of Nav channels. Notably, ST3Gal4(-/-) myocytes are shown to have higher probabilities accumulated in intermediate inactivation during the repolarization and yield a shorter refractory period than WTs. The proposed statistical design of computer experiments is generally extensible to many other disciplines that involve large-scale and computationally expensive models.

Partial Reduction in Cardiac Sialylation Contributes to Dilated Cardiomyopathy and Heart Failure

Dilated cardiomyopathy (DCM) leads to heart failure (HF) and is often associated with life‐threatening arrhythmias. Protein glycosylation is altered in DCM and with DCM risk factors. Patients with congenital disorders of glycosylation often present with DCM and related arrhythmias. We previously showed that gene deletion of the sialyltransferase, ST3Gal4, results in partial reduction in cardiac sialylation, altered cardiac Nav gating, and increased susceptibility to arrhythmias. Here, we investigated the impact of ST3Gal4 gene deletion on cardiac performance. Anomalous ECGs, including prolonged QT and corrected QT intervals, were recorded in ST3Gal4‐/‐ male mice. One‐year old ST3Gal4‐/‐ mice developed left ventricle (LV) dilation as evidenced by significantly increased LV end‐diastolic volume (EDV) and a significantly thinner LV posterior wall compared to wild type (WT). Transverse aortic constriction (TAC) was used as a stressor on 16‐20 week old animals. TAC'd ST3Gal4‐/‐ hearts decompensated rapidly, presenting by week 3 post‐surgery with significant LV dilation and reduced systolic function (together indicating HF) compared to TAC'd WT littermates (ejection fraction, EF%, ST3Gal4‐/‐= 35.3 ± 3.6%, WT = 48.3 ± 3.5%, p<0.05; EDV(µl), ST3Gal4‐/‐= 103 ± 4.6, WT = 79 ± 5.7, n = 7‐10; p<0.01). Thus, gene deletion of ST3Gal4 results in aberrant electrical signaling and to the later onset of apparent DCM. The DCM phenotype was more pronounced with stress in ST3Gal4‐/‐mice, presenting with HF by week three following pressure‐overload induction. Thus, partial reduction of sialylation contributes to aberrant cardiac electrical signaling and performance with the functional phenotype exacerbated under stress conditions that lead to the rapid onset of HF.

Disrupted calcium release as a mechanism for atrial alternans associated with human atrial fibrillation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia, but our knowledge of the arrhythmogenic substrate is incomplete. Alternans, the beat-to-beat alternation in the shape of cardiac electrical signals, typically occurs at fast heart rates and leads to arrhythmia. However, atrial alternans have been observed at slower pacing rates in AF patients than in controls, suggesting that increased vulnerability to arrhythmia in AF patients may be due to the proarrythmic influence of alternans at these slower rates. As such, alternans may present a useful therapeutic target for the treatment and prevention of AF, but the mechanism underlying alternans occurrence in AF patients at heart rates near rest is unknown. The goal of this study was to determine how cellular changes that occur in human AF affect the appearance of alternans at heart rates near rest. To achieve this, we developed a computational model of human atrial tissue incorporating electrophysiological remodeling associated with chronic AF (cAF) and performed parameter sensitivity analysis of ionic model parameters to determine which cellular changes led to alternans. Of the 20 parameters tested, only decreasing the ryanodine receptor (RyR) inactivation rate constant (kiCa) produced action potential duration (APD) alternans seen clinically at slower pacing rates. Using single-cell clamps of voltage, fluxes, and state variables, we determined that alternans onset was Ca2+-driven rather than voltage-driven and occurred as a result of decreased RyR inactivation which led to increased steepness of the sarcoplasmic reticulum (SR) Ca2+ release slope. Iterated map analysis revealed that because SR Ca2+ uptake efficiency was much higher in control atrial cells than in cAF cells, drastic reductions in kiCa were required to produce alternans at comparable pacing rates in control atrial cells. These findings suggest that RyR kinetics may play a critical role in altered Ca2+ homeostasis which drives proarrhythmic APD alternans in patients with AF.

Progesterone Within Ovulatory Menstrual Cycles Needed for Cardiovascular Protection: An Evidence-Based Hypothesis

Women experience acute myocardial infarctions (AMI) 10 years later than men – evidence that estrogen is protective is not consistent. Ovulatory disturbances (low progesterone but normal estradiol levels) silently occur in >33% of all cycles. Progesterone-based (cycle-timed serum or saliva) levels or urinary metabolite excretions are necessary to diagnose silent ovulatory disturbances within regular, normal length menstrual cycles. Progesterone acts biphasically in vitro – initial proliferation changes to differentiation. It also suppresses or complements estradiol’s actions. Basic and clinical studies show that progesterone is positively related to endothelial function/blood flow, influences vascular smooth muscle cells and cardiac electrical signals. Several studies in primates document that high fat-fed subordinate females have higher stress, fewer ovulatory cycles and lower progesterone levels but similar cycle lengths as menstruating, dominant females. Subordinate females develop arterial plaque similar to high fat-fed males. In population-based prospective data, women with early pregnancy progesterone deficiency, stress and multiple miscarriages are at five-fold higher AMI risk. Also, early menopausal AMI are associated with significantly more pre-/perimenopausal anovulatory cycles. Given the multi-dimensional positive effects of progesterone on the cardiovascular system (CVS), and persuasive data from primate and human studies associating increased AMI with ovulatory disturbances, this review presents a new CVS protection hypothesis for women – ovulatory cycles with balanced progesterone-estradiol levels decrease women’s risks for AMI. Based on this new theory, it is believed that progesterone as well as estradiol is required during the premenopausal years to prevent women’s early heart disease.

In-Silico Modeling of Glycosylation Modulation Dynamics in hERG Ion Channels and Cardiac Electrical Signals

Cardiac action potentials (AP) are produced by the orchestrated functions of ion channels. A slight change in ion channel activity may affect the AP waveform, thereby potentially increasing susceptibility to abnormal cardiac rhythms. Cardiac ion channels are heavily glycosylated, with up to 30% of a mature protein's mass comprised of glycan structures. However, little is known about how reduced glycosylation impacts the gating of hERG (human ether-a-go-go related gene) channel, which is partially responsible for late phase 2 and phase 3 of the AP. This paper integrates the data from in vitro experiments with in-silico models to predict the glycosylation modulation dynamics in hERG ion channels and cardiac electrical signals. The gating behaviors of hERG channels expressed in Chinese Hamster Ovary (CHO) cells were measured under four glycosylation conditions, i.e., full glycosylation, reduced sialylation, mannose-rich. and N-glycanase treated. Further, we developed in-silico models to simulate glycosylation-channel interactions and predict the effects of reduced glycosylation on multiscale cardiac processes (i.e., cardiac cells, 1-D and 2-D tissues). From the in-silico models, reduced glycosylation was shown to shorten the repolarization phase of cardiac APs, thereby influencing electrical propagation in cardiac fibers and tissues. In addition, the patterns of derived electrocardiogram show that reduced glycosylation of hERG channel shortens the QT interval and decreases the re-entry rate of spiral waves. This work suggests new pharmaceutical targets for the long QT syndrome and potentially other cardiac disorders.