| Autor: |
Ríos Muñoz, Gonzalo Ricardo, Ávila Alonso, Pablo, Carta Bergaz, Alejandro, Soto Flores, Nina, González-Torrecilla, Esteban, Atienza, Felipe, Fernández Avilés, Francisco, Arenal Maíz, Ángel |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Popis: |
Proceeding of: Heart Rhythm 2022: April 29-May 1, San Francisco, California Background. Identifying driving and initiating areas in persistent atrial fibrillation (AF) might contribute to better understanding AF and improve the ablation treatment. However, their correct identification still requires visual inspection or heavy signal processing algorithms that sometimes distort the electrograms (EGMs) information. In this line, artificial intelligence has recently proven to be a powerful tool that outperforms current detection methods. Objective. To automatically detect AF rotational activity (RA) drivers (rotors) with a convolutional neural network that employs raw multi-electrode EGMs without signal pre-processing. Methods. We trained 2 different CNN-based models, using 44,660 unipolar and bipolar EGMs respectively. EGMs were acquired with a multi-electrode catheter from 49 persistent AF patients. RA was annotated by an automated algorithm based on the local activation times of unipolar EGMs acquired with a 20-pole catheter. This annotation involved time-demanding signal pre-processing and post-processing steps. The models implemented recurrent CNN-based layers with a sigmoid output layer for detecting RA or no-RA Results. The CNN model trained with bipolar EGMs exhibited better accuracy than the unipolar EGMs for the test data (80.04 vs 68.40 respectively). Precision results were similar, 74.14 vs 77.91, and bipolar recall was greater than the unipolar, 92.27 vs 51.36. Conclusion. The CNN-based model allows RA driver assessment in AF patients without computationally heavy and time-consuming algorithms based on traditional signal pre-processing methods. Bipolar EGMs exhibited the best performance even though the training features and labels came from unipolar EGM data. The model could be used in real-time in new catheter ablation strategies to identify atrial substrate driving AF faster than other methods. Publicado |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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