| dc.description.abstract | Electrocardiogram based arrhythmia detection is a critical component of early diagnosis
and management of cardiovascular diseases, which continues to impose a significant
global health burden. Recent advances in data-driven deep learning techniques have
enabled highly accurate automated ECG analysis and their clinical adoption remains
limited because most models operate as black boxes and fail to provide transparent,
interpretable, and clinically meaningful explanations. Existing explainability approaches
largely rely on visual or feature-level representations and lack explicit semantic
links between identified signal patterns and medically validated cardiology concepts
restricting their usefulness for clinical reasoning and decision support. Although
ontology-driven reasoning frameworks offer structured domain knowledge and semantic
interpretation, they are rarely integrated with deep learning-based ECG analysis
systems. This review critically examines and synthesizes recent research on interpretable
ECG signal analysis, Explainable Artificial Intelligence techniques, secure diagnostic
frameworks, and ontology-based medical reasoning approaches for arrhythmia detection
with the aim of identifying key limitations that hinder clinical interoperability, trust
and real-world deployment. A structured literature review methodology is employed to
compare studies based on their interpretability strategies, use of domain knowledge, and
clinical relevance. The analysis reveals that most existing systems prioritize classification
performance and visual explainability while lacking semantic grounding, knowledge driven reasoning, and unified architectures aligned with clinical understanding. This
review highlights the need for a hybrid ontology-integrated explainable learning
framework to support transparent, clinically meaningful, and semantically grounded
ECG-based arrhythmia detection in real-world healthcare environments. | en_US |
