Comparative Analysis of Machine Learning and Deep Learning Models for Atrial Fibrillation Detection from Long-Term ECG

Atrial fibrillation is the most prevalent sustained cardiac arrhythmia and a major risk factor for stroke, heart failure, and premature mortality. Automatic detection remains challenging due to the variability of electrocardiogram (ECG) morphology, noise, and the paroxysmal nature of atrial fibrillation events. This study proposes a comprehensive framework that integrates optimised segmentation, feature extraction, and advanced deep learning architectures to improve detection accuracy. A coalescence window is introduced to dynamically cluster arrhythmic episodes, aligning computational analysis with clinical event distributions. Multiple classifiers are investigated, ranging from traditional machine learning models to state-of-the-art deep neural networks, including Temporal Convolutional Networks (TCNs), Convolutional Neural Networks (CNNs), and Bidirectional Long Short-Term Memory (BiLSTM). Experimental evaluation on a balanced dataset of ECG signals demonstrates the superior performance of deep learning models, with the best architecture achieving high accuracy and F1-score, significantly outperforming traditional approaches. Furthermore, the proposed pipeline is designed to be modular and resource-aware, supporting potential deployment in real-time and edge computing environments. These results highlight the feasibility of scalable atrial fibrillation monitoring systems that bridge algorithmic innovation with clinical applicability, ultimately contributing to earlier diagnosis and improved patient management.