A real-time signal processing software package for the electrophysiology laboratory

Kathryn D. Tiver; Campbell Strong; Dhani Dharmaprani; Darius Chapman; Evan Jenkins; Sobhan Salari Shahrbabaki; Anand N. Ganesan

doi:10.1111/jce.16281

A real-time signal processing software package for the electrophysiology laboratory

Kathryn D. Tiver, Campbell Strong, Dhani Dharmaprani, Darius Chapman, Evan Jenkins, Sobhan Salari Shahrbabaki, Anand N. Ganesan

College of Medicine and Public Health

Research output: Contribution to journal › Article › peer-review

Abstract

Background: Real-time signal processing has to date been difficult to implement in the clinical electrophysiology laboratory. To date, no open access software solutions are available in electrophysiology (EP) laboratories to facilitate real-time intraprocedural signal analysis. We aimed to develop an open access, scalable Python plug-in to allow real-time signal processing during human EP procedures.

Methods and Results: A Python-based plug in for the widely available EnsiteX mapping system was developed. This plug-in utilized the LiveSync feature of the system to allow real-time signal analysis. An open access library was developed to allow end-users to implement real-time signal analysis using this platform, implemented in the Python programming language https://github.com/anand9176/WaveWatch5000Public.

Conclusion: We have developed and demonstrated the feasibility of a readily scalable and open-access Python-based plug in to an electroanatomic mapping system (EnSiteX) to allow real-time processing and display of electrogram (EGM) based information for the procedural electrophysiologist to view intraprocedurally in the electrophysiology laboratory. The availability, to the clinician, of traditional and novel EGM-based metrics at the time of intervention, such as atrial fibrillation ablation, allows for key mechanistic insights into critical unresolved questions regarding arrhythmia mechanism.

Original language	English
Pages (from-to)	1229-1231
Number of pages	3
Journal	Journal of Cardiovascular Electrophysiology
Volume	35
Issue number	6
Early online date	23 Apr 2024
DOIs	https://doi.org/10.1111/jce.16281
Publication status	Published - Jun 2024

Keywords

arrhythmia
signal processing

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10.1111/jce.16281Licence: In Copyright

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@article{3e6559bd883045fbb2472dbc6208c5f2,

title = "A real-time signal processing software package for the electrophysiology laboratory",

abstract = "Background: Real-time signal processing has to date been difficult to implement in the clinical electrophysiology laboratory. To date, no open access software solutions are available in electrophysiology (EP) laboratories to facilitate real-time intraprocedural signal analysis. We aimed to develop an open access, scalable Python plug-in to allow real-time signal processing during human EP procedures. Methods and Results: A Python-based plug in for the widely available EnsiteX mapping system was developed. This plug-in utilized the LiveSync feature of the system to allow real-time signal analysis. An open access library was developed to allow end-users to implement real-time signal analysis using this platform, implemented in the Python programming language https://github.com/anand9176/WaveWatch5000Public. Conclusion: We have developed and demonstrated the feasibility of a readily scalable and open-access Python-based plug in to an electroanatomic mapping system (EnSiteX) to allow real-time processing and display of electrogram (EGM) based information for the procedural electrophysiologist to view intraprocedurally in the electrophysiology laboratory. The availability, to the clinician, of traditional and novel EGM-based metrics at the time of intervention, such as atrial fibrillation ablation, allows for key mechanistic insights into critical unresolved questions regarding arrhythmia mechanism.",

keywords = "arrhythmia, signal processing",

author = "Tiver, {Kathryn D.} and Campbell Strong and Dhani Dharmaprani and Darius Chapman and Evan Jenkins and Shahrbabaki, {Sobhan Salari} and Ganesan, {Anand N.}",

year = "2024",

month = jun,

doi = "10.1111/jce.16281",

language = "English",

volume = "35",

pages = "1229--1231",

journal = "Journal of Cardiovascular Electrophysiology",

issn = "1045-3873",

publisher = "Wiley-Blackwell Publishing Ltd",

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AU - Tiver, Kathryn D.

AU - Strong, Campbell

AU - Dharmaprani, Dhani

AU - Chapman, Darius

AU - Jenkins, Evan

AU - Shahrbabaki, Sobhan Salari

AU - Ganesan, Anand N.

PY - 2024/6

Y1 - 2024/6

N2 - Background: Real-time signal processing has to date been difficult to implement in the clinical electrophysiology laboratory. To date, no open access software solutions are available in electrophysiology (EP) laboratories to facilitate real-time intraprocedural signal analysis. We aimed to develop an open access, scalable Python plug-in to allow real-time signal processing during human EP procedures. Methods and Results: A Python-based plug in for the widely available EnsiteX mapping system was developed. This plug-in utilized the LiveSync feature of the system to allow real-time signal analysis. An open access library was developed to allow end-users to implement real-time signal analysis using this platform, implemented in the Python programming language https://github.com/anand9176/WaveWatch5000Public. Conclusion: We have developed and demonstrated the feasibility of a readily scalable and open-access Python-based plug in to an electroanatomic mapping system (EnSiteX) to allow real-time processing and display of electrogram (EGM) based information for the procedural electrophysiologist to view intraprocedurally in the electrophysiology laboratory. The availability, to the clinician, of traditional and novel EGM-based metrics at the time of intervention, such as atrial fibrillation ablation, allows for key mechanistic insights into critical unresolved questions regarding arrhythmia mechanism.

AB - Background: Real-time signal processing has to date been difficult to implement in the clinical electrophysiology laboratory. To date, no open access software solutions are available in electrophysiology (EP) laboratories to facilitate real-time intraprocedural signal analysis. We aimed to develop an open access, scalable Python plug-in to allow real-time signal processing during human EP procedures. Methods and Results: A Python-based plug in for the widely available EnsiteX mapping system was developed. This plug-in utilized the LiveSync feature of the system to allow real-time signal analysis. An open access library was developed to allow end-users to implement real-time signal analysis using this platform, implemented in the Python programming language https://github.com/anand9176/WaveWatch5000Public. Conclusion: We have developed and demonstrated the feasibility of a readily scalable and open-access Python-based plug in to an electroanatomic mapping system (EnSiteX) to allow real-time processing and display of electrogram (EGM) based information for the procedural electrophysiologist to view intraprocedurally in the electrophysiology laboratory. The availability, to the clinician, of traditional and novel EGM-based metrics at the time of intervention, such as atrial fibrillation ablation, allows for key mechanistic insights into critical unresolved questions regarding arrhythmia mechanism.

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A real-time signal processing software package for the electrophysiology laboratory

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