A Focused Survey of ECG Datasets for Artificial Intelligence-Based Atrial Fibrillation Detection

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia and increases the risk of stroke, heart failure, and mortality. Electrocardiography (ECG) is the most important technology for AF detection because it is inexpensive, non-invasive, and provides clinically useful information. However, the variability of ECG patterns, particularly during paroxysmal AF creates challenges in detecting AF. Artificial Intelligence (AI) offers a promising opportunity to improve AF recognition. However, AI performance is contingent on obtaining high-quality and diverse ECG datasets. This paper presents a focused survey of 15 publicly available and clinical ECG datasets used in AI-driven AF detection research between 2023 and 2025. We analyze the datasets based on acquisition methods, ECG type, format, lead configurations, annotation richness, and their application in AI models. Our comparative analysis reveals major trends, challenges such as data imbalance and motion artifacts, and gaps in current datasets including limited demographic diversity and underrepresentation of wearable ECG data. This study aims to guide future research toward more robust, interpretable, and inclusive AF detection models.