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High-Throughput Analysis of Optical Mapping Data Using ElectroMap
Author(s) -
Christopher O’Shea,
Andrew P. Holmes,
Ting Yu,
James Winter,
Simon P. Wells,
Beth A. Parker,
Dannie Fobian,
Daniel M. Johnson,
Joao Correia,
Paulus Kirchhof,
Larissa Fabritz,
Kashif Rajpoot,
Davor Pavlović
Publication year - 2019
Publication title -
journal of visualized experiments
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.596
H-Index - 91
ISSN - 1940-087X
DOI - 10.3791/59663
Subject(s) - computer science , optical mapping , software , segmentation , cardiac electrophysiology , electrophysiology , signal processing , beat (acoustics) , graphical user interface , pattern recognition (psychology) , data mining , artificial intelligence , computer hardware , digital signal processing , neuroscience , acoustics , biology , physics , programming language , genetics
Optical mapping is an established technique for high spatio-temporal resolution study of cardiac electrophysiology in multi-cellular preparations. Here we present, in a step-by-step guide, the use of ElectroMap for analysis, quantification, and mapping of high-resolution voltage and calcium datasets acquired by optical mapping. ElectroMap analysis options cover a wide variety of key electrophysiological parameters, and the graphical user interface allows straightforward modification of pre-processing and parameter definitions, making ElectroMap applicable to a wide range of experimental models. We show how built-in pacing frequency detection and signal segmentation allows high-throughput analysis of entire experimental recordings, acute responses, and single beat-to-beat variability. Additionally, ElectroMap incorporates automated multi-beat averaging to improve signal quality of noisy datasets, and here we demonstrate how this feature can help elucidate electrophysiological changes that might otherwise go undetected when using single beat analysis. Custom modules are included within the software for detailed investigation of conduction, single file analysis, and alternans, as demonstrated here. This software platform can be used to enable and accelerate the processing, analysis, and mapping of complex cardiac electrophysiology.

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