Explainable Deep Learning and Ontology Based Reasoning for ECG Arrhythmia Detection: A Comprehensive Review

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.