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Learning and detecting emergent behavior in networks of cardiac myocytes

Authors:

Scott A. Smolka,

Flavio Corradini,

Anita Wasilewska,

Emilia Entcheva,

Ezio BartocciAuthors Info & Claims

Communications of the ACM, Volume 52, Issue 3

Pages 97 - 105

https://doi.org/10.1145/1467247.1467271

Published: 01 March 2009 Publication History

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Abstract

We address the problem of specifying and detecting emergent behavior in networks of cardiac myocytes, spiral electric waves in particular, a precursor to atrial and ventricular fibrillation. To solve this problem we: (1) apply discrete mode abstraction to the cycle-linear hybrid automata (CLHA) we have recently developed for modeling the behavior of myocyte networks; (2) introduce the new concept of spatial superposition of CLHA modes; (3) develop a new spatial logic, based on spatial superposition, for specifying emergent behavior; (4) devise a new method for learning the formulae of this logic from the spatial patterns under investigation; and (5) apply bounded model checking to detect the onset of spiral waves. We have implemented our methodology as the EMERALD tool suite, a component of our EHA framework for specification, simulation, analysis, and control of excitable hybrid automata. We illustrate the effectiveness of our approach by applying EMERALD to the scalar electrical fields produced by our CELLEXCITE simulation environment for excitable-cell networks.

Supplementary Material

Learning and Detecting Emergent Behavior in Networks of Cardiac Myocytes (hscc2008.ppt)

Presentation by Radu Grosu at the 11th International Conference on Hybrid Systems: Computation and Control, St. Louis, MO, U.S.A., April 2008

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Cited By

Riaz Gondal MAtta Mehdi HKhenhrani RKumari NAli MKumar SFaraz MMalik J(2024)Role of Machine Learning and Artificial Intelligence in Arrhythmias and ElectrophysiologyCardiology in Review10.1097/CRD.0000000000000715Online publication date: 18-May-2024
https://doi.org/10.1097/CRD.0000000000000715
Matos Pedro ASilva TSequeira TLourenço JSeco JFerreira C(2024)Monitoring of spatio-temporal properties with nonlinear SAT solversInternational Journal on Software Tools for Technology Transfer (STTT)10.1007/s10009-024-00740-726:2(169-188)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10009-024-00740-7
Canfora GMercaldo FSantone A(2023)A Novel Classification Technique based on Formal MethodsACM Transactions on Knowledge Discovery from Data10.1145/359279617:8(1-30)Online publication date: 28-Jun-2023
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Learning and Detecting Emergent Behavior in Networks of Cardiac Myocytes
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Published In

cover image Communications of the ACM

Communications of the ACM Volume 52, Issue 3

Being Human in the Digital Age

March 2009

138 pages

ISSN:0001-0782

EISSN:1557-7317

DOI:10.1145/1467247

Issue’s Table of Contents

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 March 2009

Published in CACM Volume 52, Issue 3

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Cited By

Riaz Gondal MAtta Mehdi HKhenhrani RKumari NAli MKumar SFaraz MMalik J(2024)Role of Machine Learning and Artificial Intelligence in Arrhythmias and ElectrophysiologyCardiology in Review10.1097/CRD.0000000000000715Online publication date: 18-May-2024
https://doi.org/10.1097/CRD.0000000000000715
Matos Pedro ASilva TSequeira TLourenço JSeco JFerreira C(2024)Monitoring of spatio-temporal properties with nonlinear SAT solversInternational Journal on Software Tools for Technology Transfer (STTT)10.1007/s10009-024-00740-726:2(169-188)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.1007/s10009-024-00740-7
Canfora GMercaldo FSantone A(2023)A Novel Classification Technique based on Formal MethodsACM Transactions on Knowledge Discovery from Data10.1145/359279617:8(1-30)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1145/3592796
Nenzi LBartocci EBortolussi LSilvetti SLoreti M(2023)MoonLight: a lightweight tool for monitoring spatio-temporal propertiesInternational Journal on Software Tools for Technology Transfer (STTT)10.1007/s10009-023-00710-525:4(503-517)Online publication date: 1-Aug-2023
https://dl.acm.org/doi/10.1007/s10009-023-00710-5
Bartocci EBloem RMaderbacher BManjunath NNičković D(2022)Adaptive testing for specification coverage and refinement in CPS modelsNonlinear Analysis: Hybrid Systems10.1016/j.nahs.2022.10125446(101254)Online publication date: Nov-2022
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Bussi LCiancia VGadducci FLatella DMassink M(2022)On Binding in the Spatial Logics for Closure SpacesLeveraging Applications of Formal Methods, Verification and Validation. Verification Principles10.1007/978-3-031-19849-6_27(479-497)Online publication date: 22-Oct-2022
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Ciancia VLatella DMassink Mde Vink E(2022)Back-and-Forth in Space: On Logics and Bisimilarity in Closure SpacesA Journey from Process Algebra via Timed Automata to Model Learning10.1007/978-3-031-15629-8_6(98-115)Online publication date: 7-Sep-2022
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Treml LBartocci EGizzi A(2021)Modeling and Analysis of Cardiac Hybrid Cellular Automata via GPU-Accelerated Monte Carlo SimulationMathematics10.3390/math90201649:2(164)Online publication date: 14-Jan-2021
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