Yongfeng Yuan

The virtual heart as a platform for screening drug cardiotoxicity

To predict the safety of a drug at an early stage in its development is a major challenge as there is a lack of in vitro heart models that correlate data from preclinical toxicity screening assays with clinical results. A biophysically detailed computer model of the heart, the virtual heart, provides a powerful tool for simulating drug–ion channel interactions and cardiac functions during normal and disease conditions and, therefore, provides a powerful platform for drug cardiotoxicity screening. In this article, we first review recent progress in the development of theory on drug–ion channel interactions and mathematical modelling. Then we propose a family of biomarkers that can quantitatively characterize the actions of a drug on the electrical activity of the heart at multi‐physical scales including cellular and tissue levels. We also conducted some simulations to demonstrate the application of the virtual heart to assess the pro‐arrhythmic effects of cisapride and amiodarone. Using the model we investigated the mechanisms responsible for the differences between the two drugs on pro‐arrhythmogenesis, even though both prolong the QT interval of ECGs. Several challenges for further development of a virtual heart as a platform for screening drug cardiotoxicity are discussed.

Pro-arrhythmogenic effects of CACNA1C G1911R mutation in human ventricular tachycardia: insights from cardiac multi-scale models

Mutations in the CACNA1C gene are associated with ventricular tachycardia (VT). Although the CACNA1C mutations were well identified in patients with cardiac arrhythmias, mechanisms by which cardiac arrhythmias are generated in such genetic mutation conditions remain unclear. In this study, we identified a novel mechanism of VT resulted from enhanced repolarization dispersion which is a key factor for arrhythmias in the CACNA1C G1911R mutation using multi-scale computational models of the human ventricle. The increased calcium influx in the mutation prolonged action potential duration (APD), produced steepened action potential duration restitution (APDR) curves as well as augmented membrane potential differences among different cell types during repolarization, increasing transmural dispersion of repolarization (DOR) and the spatial and temporal heterogeneity of cardiac electrical activities. Consequentially, the vulnerability to unidirectional conduction block in response to a premature stimulus increased at tissue level in the G1911R mutation. The increased functional repolarization dispersion anchored reentrant excitation waves in tissue and organ models, facilitating the initiation and maintenance of VT due to less meandering rotor tip. Thus, the increased repolarization dispersion caused by the G1911R mutation is a primary factor that may primarily contribute to the genesis of cardiac arrhythmias in Timothy Syndrome.

Computational Cardiac Modeling Reveals Mechanisms of Ventricular Arrhythmogenesis in Long QT Syndrome Type 8: CACNA1C R858H Mutation Linked to Ventricular Fibrillation

Functional analysis of the L-type calcium channel has shown that the CACNA1C R858H mutation associated with severe QT interval prolongation may lead to ventricular fibrillation (VF). This study investigated multiple potential mechanisms by which the CACNA1C R858H mutation facilitates and perpetuates VF. The Ten Tusscher-Panfilov (TP06) human ventricular cell models incorporating the experimental data on the kinetic properties of L-type calcium channels were integrated into one-dimensional (1D) fiber, 2D sheet, and 3D ventricular models to investigate the pro-arrhythmic effects of CACNA1C mutations by quantifying changes in intracellular calcium handling, action potential profiles, action potential duration restitution (APDR) curves, dispersion of repolarization (DOR), QT interval and spiral wave dynamics. R858H “mutant” L-type calcium current (ICaL) augmented sarcoplasmic reticulum calcium content, leading to the development of afterdepolarizations at the single cell level and focal activities at the tissue level. It also produced inhomogeneous APD prolongation, causing QT prolongation and repolarization dispersion amplification, rendering R858H “mutant” tissue more vulnerable to the induction of reentry compared with other conditions. In conclusion, altered ICaL due to the CACNA1C R858H mutation increases arrhythmia risk due to afterdepolarizations and increased tissue vulnerability to unidirectional conduction block. However, the observed reentry is not due to afterdepolarizations (not present in our model), but rather to a novel blocking mechanism.

Mechanism underlying impaired cardiac pacemaking rhythm during ischemia: A simulation study

Ischemia in the heart impairs function of the cardiac pacemaker, the sinoatrial node (SAN). However, the ionic mechanisms underlying the ischemia-induced dysfunction of the SAN remain elusive. In order to investigate the ionic mechanisms by which ischemia causes SAN dysfunction, action potential models of rabbit SAN and atrial cells were modified to incorporate extant experimental data of ischemia-induced changes to membrane ion channels and intracellular ion homeostasis. The cell models were incorporated into an anatomically detailed 2D model of the intact SAN-atrium. Using the multi-scale models, the functional impact of ischemia-induced electrical alterations on cardiac pacemaking action potentials (APs) and their conduction was investigated. The effects of vagal tone activity on the regulation of cardiac pacemaker activity in control and ischemic conditions were also investigated. The simulation results showed that at the cellular level ischemia slowed the SAN pacemaking rate, which was mainly attributable to the altered Na+-Ca2+ exchange current and the ATP-sensitive potassium current. In the 2D SAN-atrium tissue model, ischemia slowed down both the pacemaking rate and the conduction velocity of APs into the surrounding atrial tissue. Simulated vagal nerve activity, including the actions of acetylcholine in the model, amplified the effects of ischemia, leading to possible SAN arrest and/or conduction exit block, which are major features of the sick sinus syndrome. In conclusion, this study provides novel insights into understanding the mechanisms by which ischemia alters SAN function, identifying specific conductances as contributors to bradycardia and conduction block.

Reducing false arrhythmia alarms in the ICU using novel signal quality indices assessment method

The physiological signals such as the electrocardiogram (ECG) and arterial blood pressure (ABP) in the ICU are often severely corrupted by noise, artifact and missing data, producing large errors in the estimation of the characteristics of the signals values, leading to false alarms in ICU. In order to solve this problem, we started with the signal quality assessment of vital signals in intensive care patients using a derived signal quality index (SQI) to reveal the degree of signal quality. And then we use the SQI-weighted residual error of Kalman filters (KF) to complete the date fusion for evaluating the heart rate (HR). Finally, the algorithm of arrhythmia false alarm reduction in ICU monitors was developed based upon the method of combining SQIs and HR estimations derived from ECG waveform and ABP waveform recorded from ICU patients. Results show that the overall True Positive Rate (TPR), True Negative Rate (TNR) and overall score for the Event-1 are respectively 65%, 82%, and 53.19, for the Event-2, the TPR, TNR and overall score are 65%, 87%, and 54.64.

Effects of amiodarone on ventricular excitation associated with the KCNJ2-linked short QT syndrome: Insights from a modelling study

Short QT syndrome (SQTS) is associated with ventricular arrhythmias that may lead to cardiac sudden death. However, effective pharmacological treatment for SQTS remains unclear. Amiodarone has emerged as the leading antiarrhythmic therapy for termination and prevention of ventricular arrhythmia in different clinical settings because of its proven efficacy and safety. The aim of this study was to investigate the effects of amiodarone on cardiac excitation of the KCNJ2-linked short QT syndrome. Effects of Kir2.1 D172N mutation-induced changes in IK1 were incorporated into human ventricular cell and tissue models that considered the intrinsic electrical heterogeneity in the left ventricle. Actions of amiodarone were simulated by implementing a simple block pore theory to simulate the drugs effects on ICaL and IKr block for several doses. In cellular simulations, current traces of IKr and ICaL and action potential duration of ENDO, M, and EPI cells were simulated in control, mutant, and amiodarone-in-action conditions. In tissue simulations, the pharmacological effects of amiodarone on the characteristics of ECG were examined. This study provides new insights into the pharmacokinetics of amiodarone for treatment of SQT3 under WT-D172N and D172N conditions.

Effects of maximal sodium and potassium conductance on the stability of Hodgkin-Huxley model.

Hodgkin-Huxley (HH) equation is the first cell computing model in the world and pioneered the use of model to study electrophysiological problems. The model consists of four differential equations which are based on the experimental data of ion channels. Maximal conductance is an important characteristic of different channels. In this study, mathematical method is used to investigate the importance of maximal sodium conductance g-Na and maximal potassium conductance g-K. Applying stability theory, and taking g-Na and g-K as variables, we analyze the stability and bifurcations of the model. Bifurcations are found when the variables change, and bifurcation points and boundary are also calculated. There is only one bifurcation point when g-Na is the variable, while there are two points when g-K is variable. The (g-Na,  g-K) plane is partitioned into two regions and the upper bifurcation boundary is similar to a line when both g-Na and g-K are variables. Numerical simulations illustrate the validity of the analysis. The results obtained could be helpful in studying relevant diseases caused by maximal conductance anomaly.

Automatic Detection of Atrial Fibrillation Based on Continuous Wavelet Transform and 2D Convolutional Neural Networks

Atrial fibrillation (AF) is the most common cardiac arrhythmias causing morbidity and mortality. AF may appear as episodes of very short (i.e., proximal AF) or sustained duration (i.e., persistent AF), either form of which causes irregular ventricular excitations that affect the global function of the heart. It is an unmet challenge for early and automatic detection of AF, limiting efficient treatment strategies for AF. In this study, we developed a new method based on continuous wavelet transform and 2D convolutional neural networks (CNNs) to detect AF episodes. The proposed method analyzed the time-frequency features of the electrocardiogram (ECG), thus being different to conventional AF detecting methods that implement isolating atrial or ventricular activities. Then a 2D CNN was trained to improve AF detection performance. The MIT-BIH Atrial Fibrillation Database was used for evaluating the algorithm. The efficacy of the proposed method was compared with those of some existing methods, most of which implemented the same dataset. The newly developed algorithm using CNNs achieved 99.41, 98.91, 99.39, and 99.23% for the sensitivity, specificity, positive predictive value, and overall accuracy (ACC) respectively. As the proposed algorithm targets the time-frequency feature of ECG signals rather than isolated atrial or ventricular activity, it has the ability to detect AF episodes for using just five beats, suggesting practical applications in the future.

Computer Modelling and Analysis of the Effects of Microgravity on Rat Ventriclular Excitation

Microgravity during spaceflight may cause cardiac arrhythmias. Experimental studies mimicking microgravity condition have shown some changes in cardiac tissue properties. However, it is unclear how such changes in the cardiac tissue are pro-arrhythmic. The aim of this study was to evaluate the functional impact of microgravity on cardiac electrical excitation by a simulation approach. Experimentally observed decrease in L-type Ca2+ channel current (ICaL), increases in Na+-K+ pump current (INaK) and the intercellular electrical coupling from rat with 4-weeks tail suspension were incorporated into computer models of rat ventricular myocytes and one— dimensional (1D) ventricular transmural strand. At the cellular level, it was shown that the changes in cellular membrane ion channels abbreviated the duration of action potentials (APDs) of cells, resulting in a reduced transmural dispersion in APD. At the 1D tissue level, these changes resulted in a mild increase in the conduction velocity of excitation waves, but a shortened QT interval in ECG with a depressed ST phase and flattened T-wave, which are consistent with experimental observations. They also caused a reduced tissue vulnerability to the genesis of unidirectional conduction block in response to a premature stimulus. Further analysis showed that among the microgravity-induced cellular changes, a reduced ICaL played a more important role in producing sick functional changes. In conclusion, this study provides new insights into understanding of impaired cardiac functions in microgravity condition during spaceflight.

A novel method for the detection of R-peaks in ECG based on K-Nearest Neighbors and Particle Swarm Optimization

Cardiovascular diseases are associated with high morbidity and mortality. However, it is still a challenge to diagnose them accurately and efficiently. Electrocardiogram (ECG), a bioelectrical signal of the heart, provides crucial information about the dynamical functions of the heart, playing an important role in cardiac diagnosis. As the QRS complex in ECG is associated with ventricular depolarization, therefore, accurate QRS detection is vital for interpreting ECG features. In this paper, we proposed a real-time, accurate, and effective algorithm for QRS detection. In the algorithm, a proposed preprocessor with a band-pass filter was first applied to remove baseline wander and power-line interference from the signal. After denoising, a method combining K-Nearest Neighbor (KNN) and Particle Swarm Optimization (PSO) was used for accurate QRS detection in ECGs with different morphologies. The proposed algorithm was tested and validated using 48 ECG records from MIT-BIH arrhythmia database (MITDB), achieved a high averaged detection accuracy, sensitivity and positive predictivity of 99.43, 99.69, and 99.72%, respectively, indicating a notable improvement to extant algorithms as reported in literatures.