A new study presents a hybrid feature engineering framework aimed at improving resource-efficient arrhythmia detection in electrocardiogram signals. This interpretable approach enhances the separability of arrhythmia types, potentially advancing diagnostic capabilities.
hybrid feature engineering for resource efficient arrhythmia detection in electrocardiogram signals an interpretable separability driven framework
Original title: “Hybrid feature engineering for resource-efficient Arrhythmia detection in electrocardiogram signals: An interpretable, separability-driven framework.”