AI FOR EARLY DETECTION OF HIDDEN ARRHYTHMIAS USING ECG DATA

Abstract

Early detection of hidden arrhythmias is crucial for preventing severe cardiovascular events, including stroke, heart failure, and sudden cardiac death. Electrocardiography (ECG) provides a non-invasive, real-time method to monitor cardiac electrical activity. However, subtle arrhythmias can be difficult to detect manually due to their transient nature and complex patterns. Artificial intelligence (AI), particularly deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), offers powerful tools for automated analysis of ECG signals. This paper reviews current AI methodologies for early detection of hidden arrhythmias, discusses challenges including data quality, signal variability, and interpretability, and explores the potential of AI-assisted ECG analysis to improve diagnosis, optimize patient monitoring, and enhance cardiovascular outcomes.