Gottfried Schatz Research Center, Division of Medical Physics and Biophysics, Medical University of Graz, Graz, Austria.
NumeriCor GmbH, Graz, Austria.
Comput Methods Programs Biomed. 2024 Sep;254:108299. doi: 10.1016/j.cmpb.2024.108299. Epub 2024 Jun 24.
Data from electro-anatomical mapping (EAM) systems are playing an increasingly important role in computational modeling studies for the patient-specific calibration of digital twin models. However, data exported from commercial EAM systems are challenging to access and parse. Converting to data formats that are easily amenable to be viewed and analyzed with commonly used cardiac simulation software tools such as openCARP remains challenging. We therefore developed an open-source platform, pyCEPS, for parsing and converting clinical EAM data conveniently to standard formats widely adopted within the cardiac modeling community.
pyCEPS is an open-source Python-based platform providing the following functions: (i) access and interrogate the EAM data exported from clinical mapping systems; (ii) efficient browsing of EAM data to preview mapping procedures, electrograms (EGMs), and electro-cardiograms (ECGs); (iii) conversion to modeling formats according to the openCARP standard, to be amenable to analysis with standard tools and advanced workflows as used for in silico EAM data. Documentation and training material to facilitate access to this complementary research tool for new users is provided. We describe the technological underpinnings and demonstrate the capabilities of pyCEPS first, and showcase its use in an exemplary modeling application where we use clinical imaging data to build a patient-specific anatomical model.
With pyCEPS we offer an open-source framework for accessing EAM data, and converting these to cardiac modeling standard formats. pyCEPS provides the core functionality needed to integrate EAM data in cardiac modeling research. We detail how pyCEPS could be integrated into model calibration workflows facilitating the calibration of a computational model based on EAM data.
电解剖标测（EAM）系统的数据在用于数字孪生模型的患者特异性校准的计算建模研究中发挥着越来越重要的作用。然而，从商业 EAM 系统导出的数据难以访问和解析。将其转换为易于使用常用心脏模拟软件工具（如 openCARP）查看和分析的格式仍然具有挑战性。因此，我们开发了一个开源平台 pyCEPS，用于方便地将临床 EAM 数据解析和转换为心脏建模社区广泛采用的标准格式。
pyCEPS 是一个基于 Python 的开源平台，提供以下功能：（i）访问和查询从临床映射系统导出的 EAM 数据；（ii）高效浏览 EAM 数据，预览映射过程、电图（EGM）和心电图（ECG）；（iii）根据 openCARP 标准转换为建模格式，以便与用于模拟 EAM 数据的标准工具和高级工作流程进行分析。为新用户提供了访问此补充研究工具的文档和培训材料。我们首先描述了技术基础，并展示了 pyCEPS 的功能，然后展示了它在一个示例建模应用中的使用，我们使用临床成像数据构建了一个患者特异性解剖模型。
我们提供了一个用于访问 EAM 数据并将其转换为心脏建模标准格式的开源框架。pyCEPS 提供了将 EAM 数据集成到心脏建模研究中的核心功能。我们详细说明了 pyCEPS 如何集成到模型校准工作流程中，以促进基于 EAM 数据的计算模型的校准。
