Improved detection of paroxysmal atrial fibrillation using a semi-supervised model based on ECG signals
DOI:
https://doi.org/10.26754/jjii3a.202410667Abstract
The aim of this work is to evaluate whether a semi-supervised learning model improves the performance of a supervised one in the diagnosis of paroxysmal atrial fibrillation, a task in which training data are scarce and the proportion of cases is unbalanced.
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Published
2024-07-17
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Section
Artículos (Ingeniería Biomédica)
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Copyright (c) 2024 Sara Artal Gracia, Juan Pablo Martínez Cortés, Antonio Miguel Artiaga, Julia Ramírez García
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
Artal Gracia, S., Martínez Cortés, J. P., Miguel Artiaga, A., & Ramírez García, J. (2024). Improved detection of paroxysmal atrial fibrillation using a semi-supervised model based on ECG signals. Jornada De Jóvenes Investigadores Del I3A, 12. https://doi.org/10.26754/jjii3a.202410667
