FAViTNet: An Effective Deep Learning Framework for Non-Invasive Fetal Arrhythmia Diagnosis
Analyzing fetal electrocardiograms (fECG) to classify fetal arrhythmia is a challenging task; still, it is indispensable for evaluating fetal cardiac health status. This study intends to develop a framework for the effective discernment of fetal arrhythmia that assists obstetricians in diagnosing wh...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10967483/ |
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