An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis
Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose signifi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/7/2/62 |
| _version_ | 1797564079066316800 |
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| author | Amirhossein Koneshloo Dongping Du Yuncheng Du |
| author_facet | Amirhossein Koneshloo Dongping Du Yuncheng Du |
| author_sort | Amirhossein Koneshloo |
| collection | DOAJ |
| description | Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance. |
| first_indexed | 2024-03-10T18:52:21Z |
| format | Article |
| id | doaj.art-6a1b0de07a2846caae3d81e5f3b71cf7 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-10T18:52:21Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-6a1b0de07a2846caae3d81e5f3b71cf72023-11-20T05:01:52ZengMDPI AGBioengineering2306-53542020-06-01726210.3390/bioengineering7020062An Uncertainty Modeling Framework for Intracardiac Electrogram AnalysisAmirhossein Koneshloo0Dongping Du1Yuncheng Du2Department of Industrial, Manufacturing and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USADepartment of Industrial, Manufacturing and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USADepartment of Chemical & Biomolecular Engineering, Clarkson University, Potsdam, NY 13699, USAIntracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance.https://www.mdpi.com/2306-5354/7/2/62statistical modelingmaximum likelihood estimationintracardiac electrogram analysisuncertainty analysis |
| spellingShingle | Amirhossein Koneshloo Dongping Du Yuncheng Du An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis Bioengineering statistical modeling maximum likelihood estimation intracardiac electrogram analysis uncertainty analysis |
| title | An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis |
| title_full | An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis |
| title_fullStr | An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis |
| title_full_unstemmed | An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis |
| title_short | An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis |
| title_sort | uncertainty modeling framework for intracardiac electrogram analysis |
| topic | statistical modeling maximum likelihood estimation intracardiac electrogram analysis uncertainty analysis |
| url | https://www.mdpi.com/2306-5354/7/2/62 |
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