Knowledge distillation can mitigate catastrophic forgetting in domain continual segmentation by transferring knowledge from the older model to the newer model. However, existing distillation-based methods primarily emphasize knowledge retention while overlooking inherent defects in the older teacher models. As a result, these teacher-originated defects, such as knowledge gaps or biases, are propagated and exacerbate forgetting. To address this challenge, we propose a Targeted Knowledge Rectification Learning framework (TKRL) to probe and correct teacher-originated defects. TKRL consists of two modules: 1) Probe-augmented Class Distillation, which generates gradient-driven "probes" to uncover underrepresented features in the older model, thereby bridging knowledge gaps by distilling hidden information into the new model; 2) Variance-guided Masked Autoencoder, which selectively masks and reconstructs critical high-uncertainty patches across multi-level semantic regions, thereby correcting biases inherited from the older model. Our experimental results show that TKRL effectively rectifies knowledge gaps and biases, thereby mitigating catastrophic forgetting and enhancing performance in domain continual segmentation.

The right atrium (RA) is critical for cardiac hemodynamics but is often overlooked in clinical diagnostics. This study presents a benchmark framework for RA cavity segmentation from late gadolinium-enhanced magnetic resonance imaging (LGE-MRIs), leveraging a two-stage strategy and a novel 3D deep learning network, RASnet. The architecture addresses challenges in class imbalance and anatomical variability by incorporating multi-path input, multi-scale feature fusion modules, Vision Transformers, context interaction mechanisms, and deep supervision. Evaluated on datasets comprising 354 LGE-MRIs, RASnet achieves SOTA performance with a Dice score of 92.19% on a primary dataset and demonstrates robust generalizability on an independent dataset. The proposed framework establishes a benchmark for RA cavity segmentation, enabling accurate and efficient analysis for cardiac imaging applications. Open-source code (https://github.com/zjinw/RAS) and data (https://zenodo.org/records/15524472) are provided to facilitate further research and clinical adoption.

Heart failure alters the electrophysiological properties of cardiomyocytes, leading to changes in the overall mechanical function of the heart, with significant implications for human health. Current research on heart failure predominantly focuses on two-dimensional electrophysiological models, which limits their ability to fully capture the complexity of heart failure. In contrast, our work integrates both electrophysio-logical and mechanical aspects by developing a cardiomyocyte model incorporating heart failure remodeling within a three-dimensional electromechanical coupling model, providing a more comprehensive understanding of heart failure dynamics. We investigated the variations in electromechanical coupling properties of the left ventricle under heart failure conditions, focusing on key indicators such as ventricular action potentials, myocardial contractility, ventricular volume, and pressure. The simulation results successfully reproduced the impact of heart failure on both electrophysiological and mechanical characteristics. Additionally, the simulation observed reentrant waves under heart failure conditions, further revealing that heart failure predisposes the heart to reentrant arrhythmias. In conclusion, the 3D electromechanical coupling model presented in this study offers crucial insights into the mechanisms of reentry phenomena and arrhythmogenesis under heart failure conditions.

Recently, self-training strategies have shown outstanding performance in the unsupervised medical image registration field. These strategies use their own network to generate pseudo-displacement fields (PFs) to supervise network training. However, limited diversity and accuracy of these PFs hinder their effectiveness. To address these limitations, we propose a novel mutual learning registration paradigm (MutualReg), where knowledge is distilled mutually between teacher and student networks for alternate improvement via recursive training. This involves two fundamental challenges: 1) how to generate more diverse and accurate PFs; and 2) how to effectively integrate knowledge distillation from the teacher network and learning from the student network. For the former, we employ a different and powerful teacher network thanks to the decoupling nature of MutualReg. For the latter, we introduce a Voxel-wise Reliability Criterion (VRC) module to retain reliable voxel locations of knowledge distillation. In the abdominal CT registration task, MutualReg outperforms state-of-the-art competitors, demonstrating its effectiveness. Code is available from https://github.com/PerceptionComputingLab/MutualReg/.

Despite the extensive utilization of deep learning in medical image segmentation, the achieved accuracy remains inadequate for clinical requirements due to the scarcity of annotated data, which constrains the acquisition of anatomical knowledge. Leveraging anatomical information is particularly advantageous in medical image segmentation, especially for multi-modal and cross-domain tasks. To better capture and represent anatomical structures, we propose a Swin Transformer-based anatomical structure-aware network, AnatSwin, which adopts a unique approach by utilizing label images as inputs. Compared with gray-scale images, label images, devoid of intensity information, explicitly enhance the representation of anatomical shape and spatial tissue relationships, offering valuable resources for learning anatomical structures effectively and allowing the model to concentrate on understanding morphological and spatial relationship cues. AnatSwin follows an encoder–decoder architecture, where the encoder incorporates two branches that share weights. The Swin-Transformer block serves as the basic unit of the encoder, accepting both the template label (representing the correct anatomical structure) and the pseudo label (generated by a registration model) as inputs. In order to facilitate efficient interaction among features at the same hierarchy, an attention-based feature interaction (FI) block is introduced. FI block enhances the model’s ability to capture anatomical structure by promoting feature interactions within the two branches. Furthermore, the decoder employs FI blocks to learn relationships between features at the same hierarchy, ultimately improving the segmentation performance. Experimental evaluations demonstrate that the proposed AnatSwin outperforms state-of-the-art models, highlighting its significant potential in improving the learning and representation of anatomical structures, as well as optimizing tasks related to medical image segmentation. This work signifies a promising step forward in addressing the challenges of medical image segmentation and paves the way for further advancements in the field.

Patients with myocardial ischemia and infarction are at increased risk of arrhythmias, which in turn, can exacerbate the overall risk of mortality. Despite the observed reduction in recurrent arrhythmias through antiarrhythmic drug therapy, the precise mechanisms underlying their effectiveness in treating ischemic heart disease remain unclear. Moreover, there is a lack of specialized drugs designed explicitly for the treatment of myocardial ischemic arrhythmia. This study employs an electrophysiological simulation approach to investigate the potential antiarrhythmic effects and underlying mechanisms of various pharmacological agents in the context of ischemia and myocardial infarction (MI). Based on physiological experimental data, computational models are developed to simulate the effects of a series of pharmacological agents (amiodarone, telmisartan, E-4031, chromanol 293B, and glibenclamide) on cellular electrophysiology and utilized to further evaluate their antiarrhythmic effectiveness during ischemia. On 2D and 3D tissues with multiple pathological conditions, the simulation results indicate that the antiarrhythmic effect of glibenclamide is primarily attributed to the suppression of efflux of potassium ion to facilitate the restitution of [K+]o, as opposed to recovery of IKATP during myocardial ischemia. This discovery implies that, during acute cardiac ischemia, pro-arrhythmogenic alterations in cardiac tissue's excitability and conduction properties are more significantly influenced by electrophysiological changes in the depolarization rate, as opposed to variations in the action potential duration (APD). These findings offer specific insights into potentially effective targets for investigating ischemic arrhythmias, providing significant guidance for clinical interventions in acute coronary syndrome.

The shortage of annotated ECG data presents a significant impediment, hampering the overall generalization capabilities of machine learning models tailored for automated ECG classification. The collective integration of multisource datasets presents a potential remedy for this challenge. However, it is crucial to underscore that the mere addition of supplementary data does not automatically guarantee performance enhancement, given the unresolved challenges associated with multisource data. In this research, we address one such challenge, namely, the issue of incomplete labels arising from the diversity of annotations within multi-source ECG datasets. First, we identified three distinct types of label missing: dataset-related label missing, supertype missing, and subtype missing. To address the supertype missing effectively, we introduce a novel approach known as offline category mapping which leverages the hierarchical relationships inherent within the categories to recover the missing supertype labels. Additionally, two complementary strategies, referred to as prediction masking and online category mapping, are proposed to mitigating the adverse effects of subtype and dataset-related label missing on model optimization. These strategies enhance the model's ability to identify missing subtypes under conditions of weak supervision. These pioneering methodologies are integrated into a deep learning-based framework designed for multilabel ECG classification. The performance of our proposed framework is rigorously evaluated using realistic multi-source datasets obtained from the PhysioNet/CinC challenge 2020/2021. The proposed learning framework exhibits a notable improvement in macro-average precision, surpassing the corresponding baseline model by more than 25 % on the test datasets. As a result, this research study makes a substantial contribution to the field of ECG classification by addressing the critical issue of incomplete labels in multisource datasets, ultimately enhancing the generalization capabilities of machine learning models in this domain.

Biological pacemaker is a therapy for cardiac rhythm disease, which can be transformed from ventricular myocytes (VMs) by overexpressing HCN gene which codes the expression of hyperpolarization-activated current (${\mathrm {I}}_{\mathrm{f}}$) and knocking off Kir2.1 gene which codes inward-rectifier potassium current (${\mathrm {I}}_{\mathrm{K1}}$). Our previous study built a biological pacemaker single cell model and clarified the underlying mechanisms of how gene expressing levels influence the pacemaking activity of single pacemaker cell. But the pacemaking ability of pacemaker tissue has not been researched systematically. And what factors may have effects on pacemaker’s synchronization and spontaneous beating propagation are not clear. Biological research indicated that both sinoatrial node and pacemaker cells has less expression of connexin than unexcitable cardiac cells, which provides a possibility that improve pacemaking ability of pacemaker by decreasing its cell coupling. Another possible factor is the number of pacemaker cells. According to the common sense, increasing cell number can promote pacemaking behaviours, but overmuch pacemaker cells is unreasonable in clinic. As a result, the balance between pacemaker number and cell coupling is important when applying biological pacemaker. In this study, we constructed a two-dimensional cardiac tissue model with the description of electrophysiology to illustrate the relationship between gap junction and cell number. Based on this model, we modified the cell coupling between pacemaker cells by adjusting the diffusion coefficient of tissue with different pacemaker number. In different condition, the synchronization, pacemaking cycle length and electrical signal propagation were evaluated. It can be concluded that weakening cell coupling among pacemaker cells can lift the efficiency of bio-pacemaker therapy. This study may contribute to produce effective pacemaker in clinic.

Myocardial ischemia, injury and infarction (MI) are the three stages of acute coronary syndrome (ACS). In the past two decades, a great number of studies focused on myocardial ischemia and MI individually, and showed that the occurrence of reentrant arrhythmias is often associated with myocardial ischemia or MI. However, arrhythmogenic mechanisms in the tissue with various degrees of remodeling in the ischemic heart have not been fully understood. In this study, biophysical detailed single-cell models of ischemia 1a, 1b, and MI were developed to mimic the electrophysiological remodeling at different stages of ACS. 2D tissue models with different distributions of ischemia and MI areas were constructed to investigate the mechanisms of the initiation of reentrant waves during the progression of ischemia. Simulation results in 2D tissues showed that the vulnerable windows (VWs) in simultaneous presence of multiple ischemic conditions were associated with the dynamics of wave propagation in the tissues with each single pathological condition. In the tissue with multiple pathological conditions, reentrant waves were mainly induced by two different mechanisms: one is the heterogeneity along the excitation wavefront, especially the abrupt variation in conduction velocity (CV) across the border of ischemia 1b and MI, and the other is the decreased safe factor (SF) for conduction at the edge of the tissue in MI region which is attributed to the increased excitation threshold of MI region. Finally, the reentrant wave was observed in a 3D model with a scar reconstructed from MRI images of a MI patient. These comprehensive findings provide novel insights for understanding the arrhythmic risk during the progression of myocardial ischemia and highlight the importance of the multiple pathological stages in designing medical therapies for arrhythmias in ischemia.

Cardiovascular obstruction could lead to myocardial ischemia and myocardial infarction (MI). MI can be further divided into short-term MI stage (several days) and long-term MI stage (several months) with the development of coronary artery obstruction, and the electrophysiological characteristics in these two MI stages vary greatly. At present, there are no relevant studies on the effects of different infarct areas (size and location) on the initialization and maintenance of reentrant waves in these two MI stages. Therefore, this study aims to investigate the differences in vulnerability to reentry between these two MI stages by computer modeling and simulation. Firstly, single cell models, based on the TP06 model were developed in two different MI stages. And simulation results on single-cells showed that the action potential duration (APD) significantly shortened and the resting potential (RP) elevated in the short-term MI stage, compared with that in the normal condition. However, APD prolonged and RP only changed little in the long-term MI stage. When MI areas in 2D annular ventricular tissues were designed with different lengths, widths and positions, the distribution of the vulnerable window (VW) in these two MI stages was investigated. The simulation results showed that the vulnerability of the two MI stages to the length and position of the infarct areas is the same. That is with the increase of the length, VW gradually increased and reached a constant value when the percentage of the length of the MI area reached 50%. And VW was the largest when the infarct area was close to the inner or outer wall. The vulnerability to the width of the infarct area in these two MI stages is different. In short-term MI, VW was the largest when the width of the infarct area was narrow or wide, while in long-term MI, VW was the largest when the width of the MI area reached half of the width of the ventricular wall. In this paper, the effect of the different infarct areas on the initialization and maintenance of reentrant waves in two different MI stages was investigated by computing simulation. This would improve the understanding of arrhythmogenicity in the MI stage and could provide new sights in arrhythmogenic mechanism of MI phases.

Myocardial ischemia and myocardial infarction (MI) are often accompanied by the occurrence of reentrant arrhythmias, which may lead to sudden cardiac death in severe cases. Previous studies show that electrical alternans can occur during myocardial ischemia and MI and may lead to arrhythmias. However, so far, the mechanism of alternans during myocardial ischemia and MI is unclear, so the related study on alternans is particularly important. Based on single-cell models previously modeled by us at three stages: ischemia 1a, 1b, and MI, the mechanism of alternans was revealed by comparing the changes in alternans at three levels: single cells, one-dimensional (1D) tissues, and two-dimensional (2D) tissues. In addition, the main factors inducing alternans were investigated, and the effect of antiarrhythmic drug glibenclamide on alternans was simulated. The simulation results on single cells of ischemia 1a, 1b and MI showed that the electrical alternans on the cell-levels were unstable electrical alternans. Simulation results in tissues showed that stable electrical alternans could occur in both 1D and 2D tissues. Simulation results showed that alternans in ischemia 1a were mainly caused by two factors: inhibition of $\mathrm{I}_{\mathrm{Na}}$ and elevation of $[\mathrm{K}^{+}]_{\mathrm{o}}$; alternans in ischemia 1b were mainly caused by two factors: inhibition of $\mathrm{I}_{\mathrm{NaK}}$ and elevation of $[\mathrm{K}^{+}]_{\mathrm{o}}$; alternans in MI were mainly caused by three factors: inhibition of $\mathrm{I}_{\mathrm{Kr}}$, inhibition of $\mathrm{I}_{\mathrm{Ks}}$, and elevation of $[\mathrm{K}^{+}]_{\mathrm{o}}$. And electrical alternans in the tissues result in a 2:1 conduction block. In addition, the simulation results showed that glibenclamide could inhibit electrical alternans in single cells and tissues. Electrical alternans during ischemia 1a, 1b and MI are caused by several currents that directly affect the action potential, and can lead to a 2:1 conduction block in tissues. Glibenclamide inhibits the occurrence of electrical alternans by inhibiting the efflux of potassium ions.

Pacemaking dysfunction (PD) may result in heart rhythm disorders, syncope or even death. Current treatment of PD using implanted electronic pacemakers has some limitations, such as finite battery life and the risk of repeated surgery. As such, the biological pacemaker has been proposed as a potential alternative to the electronic pacemaker for PD treatment. Experimentally and computationally, it has been shown that bio-engineered pacemaker cells can be generated from non-rhythmic ventricular myocytes (VMs) by knocking out genes related to the inward rectifier potassium channel current (IK1) or by overexpressing hyperpolarization-activated cyclic nucleotide gated channel genes responsible for the "funny" current (If). However, it is unclear if a bio-engineered pacemaker based on the modification of IK1- and If-related channels simultaneously would enhance the ability and stability of bio-engineered pacemaking action potentials. In this study, the possible mechanism(s) responsible for VMs to generate spontaneous pacemaking activity by regulating IK1 and If density were investigated by a computational approach. Our results showed that there was a reciprocal interaction between IK1 and If in ventricular pacemaker model. The effect of IK1 depression on generating ventricular pacemaker was mono-phasic while that of If augmentation was bi-phasic. A moderate increase of If promoted pacemaking activity but excessive increase of If resulted in a slowdown in the pacemaking rate and even an unstable pacemaking state. The dedicated interplay between IK1 and If in generating stable pacemaking and dysrhythmias was evaluated. Finally, a theoretical analysis in the IK1/If parameter space for generating pacemaking action potentials in different states was provided. In conclusion, to the best of our knowledge, this study provides a wide theoretical insight into understandings for generating stable and robust pacemaker cells from non-pacemaking VMs by the interplay of IK1 and If, which may be helpful in designing engineered biological pacemakers for application purposes.

OBJECTIVE: Detecting changes in the QRS complexes in ECG signals is regarded as a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach for evaluating the cardiac health of patients. Therefore, detecting QRS complexes in ECG signals must be accurate over short times. However, the reliability of automatic QRS detection is restricted by all kinds of noise and complex signal morphologies. The objective of this paper is to address automatic detection of QRS complexes. METHODS: In this paper, we proposed a new algorithm for automatic detection of QRS complexes using dual channels based on U-Net and bidirectional long short-term memory. First, a proposed preprocessor with mean filtering and discrete wavelet transform was initially applied to remove different types of noise. Next the signal was transformed and annotations were relabeled. Finally, a method combining U-Net and bidirectional long short-term memory with dual channels was used for the automatic detection of QRS complexes. RESULTS: The proposed algorithm was trained and tested using 44 ECG records from the MIT-BIH arrhythmia database and CPSC2019 dataset, which achieved 99.06% and 95.13% for sensitivity, 99.22% and 82.03% for positive predictivity, and 98.29% and 78.73% accuracy on the two datasets respectively. CONCLUSION: Experimental results prove that the proposed method may be useful for automatic detection of QRS complex task. SIGNIFICANCE: The proposed method not only has application potential for QRS complex detecting for large ECG data, but also can be extended to other medical signal research fields.

Sinus node ischemia is mainly characterized by slow heart rate, which is caused by ischemia-induced changes in electrophysiological properties and ion homeostasis leading to prolonged pacing cycle length. According to the available data, research on the mechanism of sinus node ischemia has been reported, but the report on the drug treatment of the disease is scarce. In order to reveal the effect of Chinese medicine (Yiqi Tongyang) in sinus node ischemia, this paper uses rabbit sinus node center and periphery models to simulate the effects of medium and high doses of the Chinese medicine in ischemia at the sub-cellular, cellular and tissue levels, and to simulate the changes in cellular action potentials and tissue pacing functions to predict the drug efficacy. Simulation results showed that 1) the Chinese medicine can effectively shorten the pacing cycle, with negligible effect on the duration of cellular action potential and maximal diastolic potential, but can cause a decrease in the maximal depolarization velocity; 2) it accelerated the activation of ischemic sinus node-atrum tissue, so that the electrical excitatory activity of the tissue return to the normal state; 3) by comparing the simulation results of medium and high doses of the Chinese medicine, it was shown that the high dose group did not increase the heart rate significantly, while the medium dose group had a significant effect on the regulation of heart rate, indicating that the Chinese medicine (Yiqi Tongyang) can effectively treat sinus node ischemic disease.

Sinus node ischemia is mainly characterized by slow heart rate, which is caused by ischemia-induced changes in electrophysiological properties and ion homeostasis leading to prolonged pacing cycle length. According to the available data, research on the mechanism of sinus node ischemia has been reported, but the report on the drug treatment of the disease is scarce. In order to reveal the effect of Chinese medicine (Yiqi Tongyang) in sinus node ischemia, this paper uses rabbit sinus node center and periphery models to simulate the effects of medium and high doses of the Chinese medicine in ischemia at the sub-cellular, cellular and tissue levels, and to simulate the changes in cellular action potentials and tissue pacing functions to predict the drug efficacy. Simulation results showed that 1) the Chinese medicine can effectively shorten the pacing cycle, with negligible effect on the duration of cellular action potential and maximal diastolic potential, but can cause a decrease in the maximal depolarization velocity; 2) it accelerated the activation of ischemic sinus node-atrum tissue, so that the electrical excitatory activity of the tissue return to the normal state; 3) by comparing the simulation results of medium and high doses of the Chinese medicine, it was shown that the high dose group did not increase the heart rate significantly, while the medium dose group had a significant effect on the regulation of heart rate, indicating that the Chinese medicine (Yiqi Tongyang) can effectively treat sinus node ischemic disease.

The biological pacemaker was a promising therapy for cardiac diseases such as sick sinus syndrome and atrioventricular block. A lot of experiments showed that pacemaker cells can be transformed from non-rhythmic cardiac cells or stem cells by gene therapy. However, at the tissue level, the electrophysiological properties between rhythmic and non-rhythmic regions are different. For example, the expression of connexin (such as Cx43) reduced in the induced-pacemaker cells which means that the pacemaker cells may have a less electrical coupling with adjacent cells. In addition, some researches indicated that the spatial distribution of pacemaker cells influenced the excitability of cardiac tissue. To the best of our knowledge, it is still unclear how the spatial distribution of bio-pacemaker cells affects the pacemaking behaviour in biological pacemaker tissue. In this study, we constructed a series of two-dimensional pacemaker-ventricle models containing different distributions of pacemaker cells to investigate the effect of spatial distribution on the pacemaking behaviour. Three kinds of models were designed in our simulations: (1) Tight model; (2) Embedded model; (3) Electrically isolated model. The pacemaking ability of cardiac tissue was measured by the least ratio of pacemaker cells needed to drive the whole tissue. Simulation results showed that electrically isolated model was the optimal model as it showed the best pacemaking ability among these three models. This study may guide the clinical use of bio-pacemaker.

The biological pacemaker was a promising therapy for cardiac diseases such as sick sinus syndrome and atrioventricular block. A lot of experiments showed that pacemaker cells can be transformed from non-rhythmic cardiac cells or stem cells by gene therapy. However, at the tissue level, the electrophysiological properties between rhythmic and non-rhythmic regions are different. For example, the expression of connexin (such as Cx43) reduced in the induced-pacemaker cells which means that the pacemaker cells may have a less electrical coupling with adjacent cells. In addition, some researches indicated that the spatial distribution of pacemaker cells influenced the excitability of cardiac tissue. To the best of our knowledge, it is still unclear how the spatial distribution of bio-pacemaker cells affects the pacemaking behaviour in biological pacemaker tissue. In this study, we constructed a series of two-dimensional pacemakerventricle models containing different distributions of pacemaker cells to investigate the effect of spatial distribution on the pacemaking behaviour. Three kinds of models were designed in our simulations: (1) Tight model; (2) Embedded model; (3) Electrically isolated model. The pacemaking ability of cardiac tissue was measured by the least ratio of pacemaker cells needed to drive the whole tissue. Simulation results showed that electrically isolated model was the optimal model as it showed the best pacemaking ability among these three models. This study may guide the clinical use of biopacemaker.

Heart failure (HF) is associated with an increased propensity for atrial fibrillation (AF), causing higher mortality than AF or HF alone. It is hypothesized that HF-induced remodelling of atrial cellular and tissue properties promotes the genesis of atrial action potential (AP) alternans and conduction alternans that perpetuate AF. However, the mechanism underlying the increased susceptibility to atrial alternans in HF remains incompletely elucidated. In this study, we investigated the effects of how HF-induced atrial cellular electrophysiological (with prolonged AP duration) and tissue structural (reduced cell-to-cell coupling caused by atrial fibrosis) remodelling can have an effect on the generation of atrial AP alternans and their conduction at the cellular and one-dimensional (1D) tissue levels. Simulation results showed that HF-induced atrial electrical remodelling prolonged AP duration, which was accompanied by an increased sarcoplasmic reticulum (SR) Ca2+ content and Ca2+ transient amplitude. Further analysis demonstrated that HF-induced atrial electrical remodelling increased susceptibility to atrial alternans mainly due to the increased sarcoplasmic reticulum Ca2+-ATPase (SERCA) Ca2+ reuptake, modulated by increased phospholamban (PLB) phosphorylation, and the decreased transient outward K+ current (Ito). The underlying mechanism has been suggested that the increased SR Ca2+ content and prolonged AP did not fully recover to their previous levels at the end of diastole, resulting in a smaller SR Ca2+ release and AP in the next beat. These produced Ca2+ transient alternans and AP alternans, and further caused AP alternans and Ca2+ transient alternans through Ca2+→AP coupling and AP→Ca2+ coupling, respectively. Simulation of a 1D tissue model showed that the combined action of HF-induced ion channel remodelling and a decrease in cell-to-cell coupling due to fibrosis increased the heart tissue's susceptibility to the formation of spatially discordant alternans, resulting in an increased functional AP propagation dispersion, which is pro-arrhythmic. These findings provide insights into how HF promotes atrial arrhythmia in association with atrial alternans.

It has been verified that biological pacemaker could be produced based on ventricular myocytes (VMs) by overexpressing HCN gene which codes the expression of hyperpolarization-activated current (If). Clinically, xenograft is in common use by which one specie' stem cell is infected with another specie's HCN gene so that the stem cell could transfer into cardiac pacemaker cell. The difference of HCN gene between species affects Ifproperties, but how the Ifproperties influence pacemaker creation is not easy to be qualified in biological experiments. In this study, we build an engineered biological pacemaker model based on a ventricular myocyte model by incorporating Ifformulation and simulated the membrane potential of biological pacemaker. The Ifof different species is simulated by modifying average half-maximal activation voltage (V1/2) of Ifactivation gate and Ifconductance (Gf). Based on the modified pacemaker model, the effect of Ifproperties on pacemaking stability and frequency is evaluated. Simulation results indicate that pacemaking ability is influenced dramatically by Ifproperties. In addition, the spontaneous pacemaking mechanism showed both membrane-clock and Ca2+-clock and its deep reason is analyzed in this paper. This study may provide a subcellular perspective for the clinical use of biological pacemaker.