Fuzzy logic in arrhythmia detection: A systematic review of techniques, applications, and clinical interpretability

Accurate and interpretable arrhythmia detection is essential for timely diagnosis and intervention, particularly in Medical Decision Support Systems (MDSS). Fuzzy logic, known for its ability to handle uncertainty and enhance interpretability, has emerged as a promising approach. This systematic literature review (SLR) investigates the role of fuzzy logic in advancing arrhythmia detection, focusing on accuracy, interpretability, and integration with computational intelligence. Following the PRISMA guidelines, 13 studies published between 2019 and 2024 were analysed to address four key questions: (Q1) the accuracy and reliability of fuzzy logic systems, (Q2) the effectiveness of hybrid systems combining fuzzy logic with computational intelligence, (Q3) the challenges in developing multi-class fuzzy logic systems, and (Q4) the impact of fuzzy logic on interpretability in MDSS. Techniques such as Adaptive Neural Fuzzy Inference Systems (ANFIS) and hybrid models with neural networks and bio-inspired algorithms were evaluated. ANFIS demonstrated near-perfect accuracy, while hybrid systems enhanced scalability and addressed multi-class classification challenges. Limitations included reliance on benchmark datasets, limited real-world validation, and insufficient focus on explainable artificial intelligence (XAI). Fuzzy logic shows strong potential for developing interpretable and robust MDSS for arrhythmia detection. Future research should prioritise advancing XAI, incorporating diverse datasets, and addressing real-world challenges to improve clinical applicability.