Qince Li

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.

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 early afterdepolarizations on ventricular tachycardia in human heart

Experimental studies suggest EADs may occur at rapid heart rates as a consequence of tachyarrhythmias. The aim of this study was to investigate the interaction between EAD and rapid reentrant excitation waves and assess its effects with electrocardiogram (ECG).The simulation results indicated that, at the cellular level, reduced repolarization reserve contributed to action potential duration (APD) prolongation (ENDO: 302ms vs. 402ms, MIDDLE: 414ms vs.>1000ms, EPI: 298ms vs. 397ms) and genesis of EADs only in MIDDLE cells. In the 3D model, EADs caused drift of rapid rotors. Multiple focal excitations arising from EADs kept regeneration of reentrant excitation waves by breaking excitation wave fronts. ECGs presented periodic features with stable reentry in control condition, but degenerated into irregular and complex patterns in EADs condition. The simulation results demonstrate that MIDDLE cells are prone to genesis of EADs at rapid heart rates, which plays an important role in degenerating ventricular tachycardia into ventricular fibrillation.

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 Combined Fully Convolutional Networks and Deformable Model for Automatic Left Ventricle Segmentation Based on 3D Echocardiography

Segmentation of the left ventricle (LV) from three-dimensional echocardiography (3DE) plays a key role in the clinical diagnosis of the LV function. In this work, we proposed a new automatic method for the segmentation of LV, based on the fully convolutional networks (FCN) and deformable model. This method implemented a coarse-to-fine framework. Firstly, a new deep fusion network based on feature fusion and transfer learning, combining the residual modules, was proposed to achieve coarse segmentation of LV on 3DE. Secondly, we proposed a method of geometrical model initialization for a deformable model based on the results of coarse segmentation. Thirdly, the deformable model was implemented to further optimize the segmentation results with a regularization item to avoid the leakage between left atria and left ventricle to achieve the goal of fine segmentation of LV. Numerical experiments have demonstrated that the proposed method outperforms the state-of-the-art methods on the challenging CETUS benchmark in the segmentation accuracy and has a potential for practical applications.

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.

Pacemaker Created in Human Ventricle by Depressing Inward-Rectifier K⁺ Current: A Simulation Study.

Cardiac conduction disorders are common diseases which cause slow heart rate and syncope. The best way to treat these diseases by now is to implant electronic pacemakers, which, yet, have many disadvantages, such as the limited battery life and infection. Biopacemaker has been expected to replace the electronic devices. Automatic ventricular myocytes (VMs) could show pacemaker activity, which was induced by depressing inward-rectifier K+ current (I K1). In this study, a 2D model of human biopacemaker was created from the ventricular endocardial myocytes. We examined the stability of the created biopacemaker and investigated its driving capability by finding the suitable size and spatial distribution of the pacemaker for robust pacing and driving the surrounding quiescent cardiomyocytes. Our results suggest that the rhythm of the pacemaker is similar to that of the single cell at final stable state. The driving force of the biopacemaker is closely related to the pattern of spatial distribution of the pacemaker.