OpenEP-Py and OpenEP-GUI: An interactive platform for the visualisation and analysis of clinical and simulated cardiac electrophysiology data. (19th May 2022)

• Record Type: Journal Article
• Authors: Smith, P; Bishop, M; Rajan, J; Kerfoot, E; Sim, I; Kotadia, I; O'hare, D; Fitzpatrick, N; Grubb, N R; Corrado, C; Connelly, A; Roney, C; Niederer, S; O'neill, M; Williams, S E
• Is Part Of: Europace. Volume 24:Supplement 1(2022)
• Publication Date: 2022-05-19
• Subjects: Arrhythmia -- Treatment -- Periodicals; Cardiac pacing -- Periodicals; Catheter ablation -- Periodicals; Heart -- Physiology -- Periodicals; Electrophysiology -- Periodicals; 617.4120645
• DOI: 10.1093/europace/euac053.050
• Languages: English
• ISSNs: 1099-5129

Abstract

Abstract: Funding Acknowledgements: Type of funding sources: Public grant(s) – National budget only. Main funding source(s): 1) King's Health Partnership Research & Development Challenge Fund Award (funded through Medical Research Council, UK) 2) British Heart Foundation, UK Background: Electroanatomic mapping data is typically stored in proprietary formats and is difficult to access and analyse. This presents a barrier to electrophysiology research. OpenEP has been developed over the past decade to address this issue, with the first public release of the software in late 2020. OpenEP provides a standard format for storing electrophysiology data as well as an extensive suite of analysis tools. However, the current implementation of OpenEP has several limitations. Firstly, it is written in Matlab and thus not accessible to researchers without a Matlab license. Secondly, it lacks a graphical interface mandating a level of programming expertise to use. Finally, it lacks interfaces for computational modelling data, limiting its use to clinical rather than simulated data. Purpose: In this work we address these limitations by developing (1) a Python-based implementation of OpenEP; (2) a graphical interface for interacting with OpenEP data and (3) interfaces for computational modelling environments. Methods: Following review of the OpenEP source code, we developed use-case documentation for each OpenEP function and the proposed graphical user interface. By inspection of simulation outputs from an open-source simulation environment (openCARP) we designed new interfaces for modelling data. The implementation of OpenEP-py and OpenEP-GUI is based on the standard scientific Python stack with a PyQt front-end. Results: OpenEP-Py is an open-source Python implementation for manipulation of electrophysiology data. The feature set is under active development and aims to match that of the Matlab implementation of OpenEP. In addition, OpenEP-Py can read data from cardiac electrophysiology simulations. The simulated data is stored in the standard OpenEP data structure, meaning analysis tools for clinical data can be used on the simulated data. Further, clinical data can be exported into the openCARP format, streamlining the creation of patient-specific models for simulations. Finally, because the software is open-source, OpenEP provides a transparent process for analysing data and thereby improves the reproducibility of studies. OpenEP-GUI (Figure 1) is a cross-platform desktop application for the visualisation and analysis of clinical and simulated cardiac electrophysiology data. It uses OpenEP-Py for loading, visualising, analysis and exporting electroanatomic mapping data. Multiple cases can be loaded via the System Manager, facilitating qualitative and quantitative comparison between cases. OpenEP-Py and OpenEP-GUI are licensed under the GNU-GPL v3 [WS1] and available online. Conclusions: OpenEP-Py and OpenEP-GUI provide a simple yet effective way to perform cardiac electrophysiology research. The software is both open-source and under active development. We welcome feedback, feature requests, and contributions from the wider electrophysiology research community.