Artificial Intelligence Techniques for An Optimized Dynamic Deep Unfold Network Model for Predicting Cardiac Arrhythmias Based On 12 Lead ECG Signals: Trends and Challenges

Abstract

Cardiac arrhythmias represent a major global health concern, often leading to severe complications such as stroke, heart failure, and sudden cardiac death. Early and accurate detection of arrhythmias using 12-lead electrocardiogram (ECG) signals is critical for timely intervention. Recent advancements in artificial intelligence (AI), particularly deep learning, have significantly improved automated arrhythmia classification. However, conventional deep neural networks often suffer from limitations such as lack of interpretability, high computational complexity, and suboptimal generalization. To address these challenges, dynamic deep unfolding networks have emerged as a promising paradigm that integrates model-based optimization techniques with data-driven learning. This paper presents a comprehensive review of AI techniques applied to an optimized dynamic deep unfolding network model for arrhythmia prediction using 12-lead ECG signals. It explores recent trends, including hybrid architectures, optimization-driven learning, and real-time clinical deployment strategies. Additionally, key challenges such as data imbalance, noise variability, model explainability, and computational constraints are critically analyzed. The study aims to provide insights into the design of efficient, interpretable, and clinically reliable arrhythmia detection systems. By synthesizing current research and identifying gaps, this work contributes toward the development of next-generation intelligent cardiac diagnostic frameworks.