(G-268) Signal Processor for Electrogram and Electroanatomic Data (SPEED)

Saturday, October 14, 2023

9:30 AM – 10:30 AM PDT

Location: Exhibit Hall - Row G - Poster # 268

Presenting Author(s)

RJ

Rui Jin (he/him/his)

Undergraduate Research Assistant
University of Utah, United States

Co-Author(s)

LR

Lindsay Rupp

Graduate Research Assistant
University of Utah, United States
AB

Anna Busatto

Graduate Research Assistant
University of Utah, United States

Primary Investigator(s)

RM

Rob Macleod

Professor of Biomedical Engineering
Scientific Computing and Imaging Institute, United States

Introduction:: The electrocardiogram is the most common tool to diagnose and assess cardiac conditions, such as rhythm abnormalities, myocardial ischemia, and heart failure. However, clinical diagnosis and management of heart disease are challenging due to the remote nature of body-surface electrocardiogram measurements, with a median accuracy of 67% among physicians. One approach to improve the accuracy of electrocardiography is to conduct mapping studies in which 10-100 catheter-based electrodes are inserted within the heart. The recorded signals provide more proximity and thus accuracy, but they also require specialized software to analyze, quantify, and visualize. We developed the Signal Processor for Electrogram and Electroanatomic Data (SPEED), a new, open-source, unified pipeline to facilitate effective signal processing and visualization of such cardiac-mapping signals.

Materials and Methods:: Our pipeline is based on two existing toolboxes, the Preprocessing Framework for Electrograms Intermittently Fiducialized from Experimental Recordings (PFEIFER) and OpenEP. PFEIFER is a toolset for sophisticated signal-processing of cardiac electrograms that allows the user to select semi-automatically fiducial markers, which are time points and intervals of interest within a heartbeat. OpenEP primarily accepts as input complete electroanatomic data, including both processed cardiac electrograms and spatial geometry; OpenEP also provides built-in functions for analyzing and visualizing cardiac electrograms, such as displaying potentials on the cardiac geometry. Since both software packages provide complementary workflows for managing electrograms, our goal was to integrate the two software packages and present it to the user as a new Graphic User Interface utilizing both applications simultaneously.

Results, Conclusions, and Discussions:: It was natural to develop SPEED in MATLAB as this is also the language used for both PFEIFER and OpenEP. The primary interface to SPEED incorporates a data-centric design such that the user can provide the electrogram and geometry files to be processed, and the algorithm automatically determines the applicable functions based on the input type. Since both PFEIFER and OpenEP can parse data into more interpretable open-source formats, the user can also export the processed data for further analysis in addition to visualizing and quantifying the data features. Through integrating both software packages, SPEED can support the following main functionalities: (1) in-depth filtering and processing of electrogram signals, (2) visualizing anatomic geometry and electrode locations, and (3) mapping three-dimensional potential and activation of cardiac electrophysiology. SPEED offers the user a more thorough and unified workflow in the analysis of cardiac-mapping signals than either of its components. The user can utilize the functionalities of both PFEIFER and OpenEP simultaneously, allowing for a versatile and powerful processing pipeline. For instance, the user can extract key features from the recorded electrograms and visualize the location of the corresponding electrodes, a feature that was previously not possible. In addition, the open-source nature of the software packages allows the user to modify or expand the functions to better suit their individual needs. The software design of SPEED is still in the early stages; thus, as with most software, further development and user testing will follow to make the algorithms compatible with more data types and implement additional features. SPEED processes and displays the complex information in a clear and accessible way, allowing the user to perform subsequent interpretations and analyses more easily. SPEED can be used by research cardiologists to facilitate a more efficient workflow, as well as to improve the efficiency and accuracy of clinical diagnosis of heart diseases.

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