High-Throughput Analysis of Optical Mapping Data Using ElectroMap
Related Experiment Video
Updated: Jan 23, 2026

High-Throughput Analysis of Optical Mapping Data Using ElectroMap
Published on: June 4, 2019
Christopher O'Shea1, Andrew P Holmes2, Ting Y Yu3
1Institute of Cardiovascular Sciences, University of Birmingham; EPSRC Centre for Doctoral Training in Physical Sciences for Health, School of Chemistry, University of Birmingham; School of Computer Science, University of Birmingham.
Abstract:
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.
Related Concept Videos
Analysis of Population Pharmacokinetic Data
Overview of Microsoft Excel as a Data Analysis Tool
Statistical Software for Data Analysis and Clinical Trials
Performing a Simple Data Analysis using MS-Excel Function
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
How Data are Classified: Numerical Data
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...

