Classification of cardiac excitation patterns during atrial fibrillation
The goal of this research was to classify cardiac excitation patterns during atrial fibrillation (AFib). For this purpose, virtual models of intracardiac mapping catheters were moved across in-silico cardiac tissue to extract local activation times (LATs) of each catheter electrode from simulated ca...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2016-09-01
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| Series: | Current Directions in Biomedical Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/cdbme-2016-0037 |
| _version_ | 1819291542577217536 |
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| author | Reich Christian Oesterlein Tobias Rottmann Markus Seemann Gunnar Dössel Olaf |
| author_facet | Reich Christian Oesterlein Tobias Rottmann Markus Seemann Gunnar Dössel Olaf |
| author_sort | Reich Christian |
| collection | DOAJ |
| description | The goal of this research was to classify cardiac excitation patterns during atrial fibrillation (AFib). For this purpose, virtual models of intracardiac mapping catheters were moved across in-silico cardiac tissue to extract local activation times (LATs) of each catheter electrode from simulated cardiac action potential (AP) signals. The resulting LAT patterns consisting of the LATs of all electrodes resemble patterns measured in clinical cases. The LATs represent the input information for features that were used to separate four different excitation patterns during AFib. Those four excitation patterns were plane wave, ectopic focus (spherical wave), rotor (spiral wave) and block. A feature selection algorithm was used to investigate the features concerning their power to classify the different simulated excitation patterns. The scores of the selected features were used to train and optimize a support vector machine (SVM). The optimized and cross-validated SVM was then used to classify the simulated cardiac excitation patterns. The achieved overall classification accuracy of this SVM model was 98.4 %. |
| first_indexed | 2024-12-24T03:40:18Z |
| format | Article |
| id | doaj.art-ad007b399a304a3eb6ecb082dd2c8a48 |
| institution | Directory Open Access Journal |
| issn | 2364-5504 |
| language | English |
| last_indexed | 2024-12-24T03:40:18Z |
| publishDate | 2016-09-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Current Directions in Biomedical Engineering |
| spelling | doaj.art-ad007b399a304a3eb6ecb082dd2c8a482022-12-21T17:16:55ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042016-09-012116116610.1515/cdbme-2016-0037cdbme-2016-0037Classification of cardiac excitation patterns during atrial fibrillationReich Christian0Oesterlein Tobias1Rottmann Markus2Seemann Gunnar3Dössel Olaf4Karlsruhe Institute of Technology, Institute of Biomedical Engineering, Karlsruhe, GermanyKarlsruhe Institute of Technology, Institute of Biomedical Engineering, Karlsruhe, GermanyKarlsruhe Institute of Technology, Institute of Biomedical Engineering, Karlsruhe, GermanyKarlsruhe Institute of Technology, Institute of Biomedical Engineering, Karlsruhe, GermanyKarlsruhe Institute of Technology, Institute of Biomedical Engineering, Karlsruhe, GermanyThe goal of this research was to classify cardiac excitation patterns during atrial fibrillation (AFib). For this purpose, virtual models of intracardiac mapping catheters were moved across in-silico cardiac tissue to extract local activation times (LATs) of each catheter electrode from simulated cardiac action potential (AP) signals. The resulting LAT patterns consisting of the LATs of all electrodes resemble patterns measured in clinical cases. The LATs represent the input information for features that were used to separate four different excitation patterns during AFib. Those four excitation patterns were plane wave, ectopic focus (spherical wave), rotor (spiral wave) and block. A feature selection algorithm was used to investigate the features concerning their power to classify the different simulated excitation patterns. The scores of the selected features were used to train and optimize a support vector machine (SVM). The optimized and cross-validated SVM was then used to classify the simulated cardiac excitation patterns. The achieved overall classification accuracy of this SVM model was 98.4 %.https://doi.org/10.1515/cdbme-2016-0037atrial fibrillationclassificationexcitation patternlocal activation timesupport vector machine |
| spellingShingle | Reich Christian Oesterlein Tobias Rottmann Markus Seemann Gunnar Dössel Olaf Classification of cardiac excitation patterns during atrial fibrillation Current Directions in Biomedical Engineering atrial fibrillation classification excitation pattern local activation time support vector machine |
| title | Classification of cardiac excitation patterns during atrial fibrillation |
| title_full | Classification of cardiac excitation patterns during atrial fibrillation |
| title_fullStr | Classification of cardiac excitation patterns during atrial fibrillation |
| title_full_unstemmed | Classification of cardiac excitation patterns during atrial fibrillation |
| title_short | Classification of cardiac excitation patterns during atrial fibrillation |
| title_sort | classification of cardiac excitation patterns during atrial fibrillation |
| topic | atrial fibrillation classification excitation pattern local activation time support vector machine |
| url | https://doi.org/10.1515/cdbme-2016-0037 |
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