Data Sheet 1_ECG-XPLAIM: eXPlainable Locally-adaptive Artificial Intelligence Model for arrhythmia detection from large-scale electrocardiogram data.pdf

Data Sheet 1_ECG-XPLAIM: eXPlainable Locally-adaptive Artificial Intelligence Model for arrhythmia detection from large-scale electrocardiogram data.pdf

Background<p>Timely and accurate detection of arrhythmias from electrocardiograms (ECGs) is crucial for improving patient outcomes. While artificial intelligence (AI)-based ECG classification has shown promising results, limited transparency and interpretability often impede clinical adoption....

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Bibliografiska uppgifter
Huvudupphovsman: Panteleimon Pantelidis (12499942) (author)
Övriga upphovsmän: Samuel Ruipérez-Campillo (17078062) (author), Julia E. Vogt (9956963) (author), Alexios Antonopoulos (11860103) (author), Ioannis Gialamas (22445239) (author), George E. Zakynthinos (12421278) (author), Michael Spartalis (10882954) (author), Polychronis Dilaveris (14161072) (author), Jose Millet (8279028) (author), Theodore G. Papaioannou (22445242) (author), Evangelos Oikonomou (2631991) (author), Gerasimos Siasos (2631982) (author)
Publicerad: 2025
Ämnen:
Cardiology
arrhythmia
electrocardiogram
artificial intelligence
deep learning
machine learning
cardiac signals
explainability
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