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Nonlinear Dynamics and Chaos in Cardiac Physics

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Prof. Dr. Elena G. Tolkacheva

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Guest Editor

Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA
Interests: therapeutic potential of neuromodulation, specifically, vagal nerve stimulation, for treatment of cardiovascular diseases and hypertension; cardiac electrophysiology

Prof. Dr. Roman Grigoriev

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Guest Editor

School of Physics, Georgia Institute of Technology, Atlanta, GA 30332, USA
Interests: spatiotemporal chaos; pattern formation in, and control of, spatially extended systems; fluid mechanics; cardiac tissue dynamics; heat transfer

Special Issue Information

Dear Colleagues,

The heart is regulated by multiple factors at different scales both in time and space. Despite stochastic effects at the (sub)cellular scale, the heart can be viewed as a spatially extended nonlinear dynamical system, with arrhythmias being one of the natural characteristic behaviors. Arrhythmias tend to have an inherently multiscale nature that requires an understanding of the link between molecular factors, electrophysiological properties at the subcellular and cellular levels, and those at the tissue and organ levels. Various factors contribute to a different extent to the formation and maintenance of complex cardiac rhythms.

The main focus of this Special Issue is to promote collaboration and exchange of new ideas between researchers working on understanding the genesis and dynamics of complex rhythms in the heart and their control, and developing corresponding predictive descriptions. Recent years have seen a significant increase in research activities in these areas within diverse communities, including mechanical, electrical, biomedical, and computational engineering, as well as physics and mathematics. However, integrated, experimentally validated approaches that incorporate the feedback between the relevant electrophysiology at the (sub)cellular scale and dynamics at the tissue/organ scale remain rare. This Special Issue aims to catalyze innovative and integrative approaches to develop a deeper understanding of complex cardiac dynamics using techniques from physics, nonlinear dynamics, engineering, and computational science.

We welcome original research and review articles on the following research topics:

Discrimination between normal and abnormal electrical activity in the heart;
Initiation and maintenance of complex cardiac rhythms;
Developing approaches for nonlinear analysis of cardiac signals;
Analysis and characterization of arrhythmia progression in the heart;
First-principles and data-driven modeling and prediction of cardiac arrhythmias.

Prof. Dr. Elena G. Tolkacheva
Prof. Dr. Roman Grigoriev
Guest Editors

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15 pages, 2940 KiB

Open AccessArticle

A Data-Driven Preprocessing Framework for Atrial Fibrillation Intracardiac Electrocardiogram Analysis

by Xiangzhen Kong, Vasanth Ravikumar, Siva K. Mulpuru, Henri Roukoz and Elena G. Tolkacheva

Entropy 2023, 25(2), 332; https://doi.org/10.3390/e25020332 - 10 Feb 2023

Cited by 3 | Viewed by 1288

Abstract

Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Signal-processing approaches are widely used for the analysis of intracardiac electrograms (iEGMs), which are collected during catheter ablation from patients with AF. In order to identify possible targets for ablation therapy, dominant frequency (DF) is widely used and incorporated in electroanatomical mapping systems. Recently, a more robust measure, multiscale frequency (MSF), for iEGM data analysis was adopted and validated. However, before completing any iEGM analysis, a suitable bandpass (BP) filter must be applied to remove noise. Currently, no clear guidelines for BP filter characteristics exist. The lower bound of the BP filter is usually set to 3–5 Hz, while the upper bound (

{\bar{B P}}^{t h}

) of the BP filter varies from 15 Hz to 50 Hz according to many researchers. This large range of

{\bar{B P}}^{t h}

subsequently affects the efficiency of further analysis. In this paper, we aimed to develop a data-driven preprocessing framework for iEGM analysis, and validate it based on DF and MSF techniques. To achieve this goal, we optimized the

{\bar{B P}}^{t h}

using a data-driven approach (DBSCAN clustering) and demonstrated the effects of different

{\bar{B P}}^{t h}

on subsequent DF and MSF analysis of clinically recorded iEGMs from patients with AF. Our results demonstrated that our preprocessing framework with

{\bar{B P}}^{t h}

= 15 Hz has the best performance in terms of the highest Dunn index. We further demonstrated that the removal of noisy and contact-loss leads is necessary for performing correct data iEGMs data analysis. Full article

(This article belongs to the Special Issue Nonlinear Dynamics and Chaos in Cardiac Physics)

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