Deep Learning and Optimization Approaches in An Optimized Dynamic Deep Unfold Network Model for Predicting Cardiac Arrhythmias Based On 12 Lead ECG Signals: A Review

Abstract

Cardiac arrhythmias represent a significant global health concern due to their association with increased morbidity and mortality. The advent of deep learning techniques has transformed automated arrhythmia detection using 12-lead electrocardiogram (ECG) signals, enabling more accurate and efficient diagnostic systems. This paper presents a comprehensive review of deep learning and optimization approaches integrated within an optimized dynamic deep unfolding network model for arrhythmia prediction. The study explores how traditional signal processing methods have evolved into end-to-end learning frameworks, combining convolutional, recurrent, and hybrid architectures with optimization-driven unfolding strategies. Dynamic deep unfolding networks bridge model-based and data-driven paradigms, allowing interpretable and efficient learning by embedding iterative optimization procedures into neural network structures. The review further examines advancements in feature extraction, noise reduction, temporal modeling, and classification techniques tailored for multi-lead ECG data. Emphasis is placed on optimization mechanisms that enhance convergence, generalization, and computational efficiency. Comparative insights into various architectures and datasets highlight current trends, performance benchmarks, and existing challenges. The findings suggest that integrating optimization principles within deep learning frameworks significantly improves arrhythmia prediction accuracy and robustness. This review aims to guide future research toward developing clinically reliable, scalable, and interpretable diagnostic systems leveraging optimized deep unfolding models.