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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2403.10394 |
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| _version_ | 1866917614730608640 |
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| author | Arnold, Robert Prassl, Anton J. Neic, Aurel Thaler, Franz Augustin, Christoph M. Gsell, Matthias A. F. Gillette, Karli Manninger, Martin Scherr, Daniel Plank, Gernot |
| author_facet | Arnold, Robert Prassl, Anton J. Neic, Aurel Thaler, Franz Augustin, Christoph M. Gsell, Matthias A. F. Gillette, Karli Manninger, Martin Scherr, Daniel Plank, Gernot |
| contents | Background and Objective: 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. Methods and Results: 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. Conclusion: 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_10394 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | pyCEPS: A cross-platform Electroanatomic Mapping Data to Computational Model Conversion Platform for the Calibration of Digital Twin Models of Cardiac Electrophysiology Arnold, Robert Prassl, Anton J. Neic, Aurel Thaler, Franz Augustin, Christoph M. Gsell, Matthias A. F. Gillette, Karli Manninger, Martin Scherr, Daniel Plank, Gernot Medical Physics Background and Objective: 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. Methods and Results: 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. Conclusion: 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. |
| title | pyCEPS: A cross-platform Electroanatomic Mapping Data to Computational Model Conversion Platform for the Calibration of Digital Twin Models of Cardiac Electrophysiology |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2403.10394 |