An HPC-Based Approach to Study Living System Computational Model Parameter Dependency

High performance computing (HPC) allows one to run in parallel large amount of independent numerical experiments for computationally intensive simulations of a complex system. Results of such experiments can be used to derive dependencies between functional characteristics of simulated system and parameters of the computational model. In this paper, we implemented this HPC approach with using a computational model of the electrical activity in the left ventricle of human heart. To illustrate possibilities of the approach, we analyzed dependencies of electrophysiological characteristics of the left ventricle on the parameters of its geometry. Particularly, we identified a dependence of the dynamics of activated myocardium part during excitation on the model parameters of the myocardial fiber orientation in the ventricular wall

Comparison of Depolarization and Depolarization in Mathematical Models of the Left Ventricle and the Longitudinal Ventricular Slice

Myocardial slices are widely used for cardiac electrophysiology research but correspondence of electrophysiological properties between the cardiac slices and the whole heart has not been studied in details. The aim of this study is to investigate the differences in electrophysiological properties between the left ventricle and the longitudinal ventricular slice passing through the apex using mathematical models. ECG signals and the time of activation and repolarization, repolarization dispersion and dispersion of action potential duration were compared. We have shown that the electrophysiological processes in the ventricle and the longitudinal ventricular slice are quite similar, so we believe that cardiac slices can be used to evaluate global electrophysiological properties of the ventricles. The local differences obtained can be explained by differences in geometry and fiber orientation locally affecting depolarization and repolarization in the myocardium. © 2018 Creative Commons Attribution.Russian Foundation for Basic Research, RFBR: 16-31-60015, 18-31-00401This work was supported by IIF UrB RAS theme #AAAA-A18-118020590031-8, RFE Government Act #211 of March 16, 2013, the Program of the Presidium RAS #27 and RFBR (#16-31-60015, 18-31-00401)

In Silico Comparison of Phase Maps Based on Action Potential and Extracellular Potential

In this work, a computer simulation of the reentrant ventricular tachycardia (VT) was used to investigate the peculiar properties of phase maps based on transmembrane potentials (TP) and extracellular potentials (EP). The simulation approach included the bidomain model with full myocardium-torso coupling, a realistic ionic model of the human cardiomyocytes and a personalized geometry of the heart and torso. The phase mapping pipeline includes a signal detrending and the Hilbert transform. It was demonstrated that TP-based phase maps correlated well with the propagation of cardiac excitation. In contrast, EP-based phase mapping provides some aberrations which can complicate electrophysiological interpretation of the phase maps in terms of cardiac activation sequence. It was also shown that a modification of the phase computation algorithm, including the sign inversion of signals and a special transformation of the phase plot, can partially eliminate these aberrations and make EP-based phase maps resemble TP-based maps. © 2018 Creative Commons Attribution.Russian Foundation for Basic Research, RFBR: 18-31-00401The reported study was funded by RFBR according to the research project No. 18-31-00401. Development of computer model with personalized geometry was funded by IIP UrB RAS theme No AAAA-A18-118020590031-8, RF Government Act #211 of March 16, 2013 (agreement 02.A03.21.0006), Program of the Presidium RAS #27 (project AAAA-A18-118020590030-1)

Benefits of Mirror Weight Symmetry for 3D Mesh Segmentation in Biomedical Applications

3D mesh segmentation is an important task with many biomedical applications. The human body has bilateral symmetry and some variations in organ positions. It allows us to expect a positive effect of rotation and inversion invariant layers in convolutional neural networks that perform biomedical segmentations. In this study, we show the impact of weight symmetry in neural networks that perform 3D mesh segmentation. We analyze the problem of 3D mesh segmentation for pathological vessel structures (aneurysms) and conventional anatomical structures (endocardium and epicardium of ventricles). Local geometrical features are encoded as sampling from the signed distance function, and the neural network performs prediction for each mesh node. We show that weight symmetry gains from 1 to 3% of additional accuracy and allows decreasing the number of trainable parameters up to 8 times without suffering the performance loss if neural networks have at least three convolutional layers. This also works for very small training sets. © 2023 IEEE.Russian Science Foundation, RSF: RSF 22-21-00930This work has been supported by the grant of the Russian Science Foundation, RSF 22-21-00930. The computations were performed on the Uran supercomputer at the IMM UB RAS

Compressor-Based Classification for Atrial Fibrillation Detection

Atrial fibrillation (AF) is one of the most common arrhythmias with challenging public health implications. Therefore, automatic detection of AF episodes on ECG is one of the essential tasks in biomedical engineering. In this paper, we applied the recently introduced method of compressor-based text classification with gzip algorithm for AF detection (binary classification between heart rhythms). We investigated the normalized compression distance applied to RR-interval and ΔRR-interval sequences (ΔRR-interval is the difference between subsequent RR-intervals). Here, the configuration of the k-nearest neighbour classifier, an optimal window length, and the choice of data types for compression were analyzed. We achieved good classification results while learning on the full MIT-BIH Atrial Fibrillation database, close to the best specialized AF detection algorithms (avg. sensitivity = 97.1%, avg. specificity = 91.7%, best sensitivity of 99.8%, best specificity of 97.6% with fivefold cross-validation). In addition, we evaluated the classification performance under the few-shot learning setting. Our results suggest that gzip compression-based classification, originally proposed for texts, is suitable for biomedical data and quantized continuous stochastic sequences in general. © 2023 IEEE

Role of myocardial properties and pacing lead location on ECG in personalized paced heart models

Personalised cardiac models were built from the computed tomography imaging data for two patients with implanted cardiac resynchronisation therapy devices. The cardiac models comprised a biventricular model of myocardial electrophysiology coupled with a model of the torso to simulate the body surface potential map. The models were verified against electrocardiogams (ECG) recorded in the patients from 240 leads on the body surface under left ventricular pacing. The simulated ECG demonstrated a significant sensitivity to the myocardial anisotropy and location of the pacing electrode tip in the models. An apicobasal cellular heterogeneity was shown to be less significant for the ECG pattern at the paced-ventricle activation than that showed earlier by Keller and co-authors (2012) for the normal activation sequence. © 2017 IEEE Computer Society. All rights reserved.This study was supported by the RAS Presidium Programme I.33Π, and Government of the Russian Federation (agreement 02.A03.21.0006). We used the computational clusters of Ural Federal University and ”URAN” of Institute of Mathematics and Mechanics (Ekaterinburg)

Phase Mapping for Cardiac Unipolar Electrograms with Neural Network Instead of Phase Transformation

A phase mapping is an approach to processing signals of electrograms that are recorded from the surface of cardiac tissue. The main concept of the phase mapping is an application of the phase transformation with the aim to obtain signals with useful properties. In our study, we propose to use a simple sawtooth signal instead of a phase of a signal for processing of electrogram data and building of the phase maps. We denote transformation that can provide this signal as a phase-like transformation (PLT). PLT defined via a convolutional neural network that is trained on a dataset from computer models of cardiac tissue electrophysiology. The proposed approaches were validated on data from the detailed personalized model of the human torso electrophysiology. This paper includes visualization of the phase map based on PLT and shows the applicability of the proposed approaches in the analysis of the complex non-stationary periodic activity of the excitable cardiac tissue. © 2020 IEEE.The reported study was funded by RFBR, according to the research project No. 18-31-00401. Development of the mathematical models is supported by IIF UrB RAS theme №AAAA-A19-119070190064-4, RF Government Act №211 of March 16, 2013, the Program of the Presidium RAS

Additional Pathogenic Pathways in RBCK1 Deficiency

RBCK1 deficiency is a rare congenital autoinflammatory disease that causes inflammatory disruption on the molecular level. This deficiency has three major clinical manifestations: increased sensitivity to bacterial infections, autoinflammation syndrome, and the accumulation of amylopectin in skeletal muscle. The amylopectinosis causes myopathy and cardiomyopathy. The pathogenesis of the disease is poorly investigated and may include unnoticed relationships. We performed gene expression analysis on patients with RBCK1 deficiency and three other autoinflammatory diseases. The identification of differentially expressed genes revealed a large number of downregulated genes that are involved in the activation of essential metabolic and immune pathways, including NF-kB and Pi3k-Akt-mTOR. Signaling pathways were analysed using the KEGG (Kyoto Encyclopedia of Genes and Genomes) and Gene Ontology resource. Predicted protein-protein interactions were retrieved from the STRING (Search Tool for the Retrieval of Interacting proteins database). Besides the primary involvement of RBCK1 in disease pathology, several downregulated pathways aggravate symptoms of myopathy, cardiomyopathy, and bacterial disease. The studied pathways may serve as new targets for the development of compensatory therapies for patients with RBCK1 deficiency. © 2022, Mathematical Biology and Bioinformatics. All rights reserved

Effects of Lead Position, Cardiac Rhythm Variation and Drug-induced QT Prolongation on Performance of Machine Learning Methods for ECG Processing

Machine learning shows great performance in various problems of electrocardiography (ECG) signal analysis. However, collecting a dataset for biomedical engineering is a very difficult task. Any dataset for ECG processing contains from 100 to 10,000 times fewer cases than datasets for image or text analysis. This issue is especially important because of physiological phenomena that can significantly change the morphology of heartbeats in ECG signals. In this preliminary study, we analyze the effects of lead choice from the standard ECG recordings, variation of ECG during 24-hours, and the effects of QT-prolongation agents on the performance of machine learning methods for ECG processing. We choose the problem of subject identification for analysis, because this problem may be solved for almost any available dataset of ECG data. In a discussion, we compare our findings with observations from other works that use machine learning for ECG processing with different problem statements. Our results show the importance of training dataset enrichment with ECG signals acquired in specific physiological conditions for obtaining good performance of ECG processing for real applications. © 2020 IEEE.The reported study was supported by RFBR research project No. 19-37-50079 and supported by the IIF UrB RAS theme №AAAA-A18-118020590031-8, RF Government Act #211 of March 16, 2013, the Program of the Presidium RAS

STUDY OF DELAYED AFTERDEPOLARIZATION OF CARDIOMYOCITES IN 1D MODEL OF CARDIAC TISSUE

In this work, we study the spontaneous activation of cardiomyocytes in 1D model of the myocardial tissue. The simulation use Noble (1998) electrophysiology model and open source software Oxford Chaste. In the computational experiments, we variate the space distribution of the parameters which influence to cardiomyocytes delayed afterdepolarization activity. The simulations show the significant influence of healthy-pathology border properties to frequency and features of premature excitation in the model