Clinical Applicability of Machine Learning Models for Binary and Multi-Class Electrocardiogram Classification

Clinical Applicability of Machine Learning Models for Binary and Multi-Class Electrocardiogram Classification

Background: This study investigates the application of machine learning models to classify electrocardiogram signals, addressing challenges such as class imbalances and inter-class overlap. In this study, “normal” and “abnormal” refer to electrocardiogram findings that either align with or deviate f...

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Bibliographic Details
Main Authors: Daniel Nasef, Demarcus Nasef, Kennette James Basco, Alana Singh, Christina Hartnett, Michael Ruane, Jason Tagliarino, Michael Nizich, Milan Toma
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:AI
Subjects:
electrocardiogram signal classification
convolutional neural networks
machine learning in cardiology
hierarchical classification frameworks
feature importance analysis
deep learning architectures
Online Access:https://www.mdpi.com/2673-2688/6/3/59
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