Review on Advent of Artificial Intelligence in Electrocardiogram for the Detection of Extra-Cardiac and Cardiovascular Disease

Abstract

Artificial intelligence (AI) is that encompasses machine learning (ML) combined with human intelligence had begun to reform medical practices into a new dimension. Advancements and developments of AI molds improved diagnostics in the field of cardiology. Electrocardiogram (ECG) is a simple and cost-effective tool to identify cardiac disorder and which is its reason for being into practice till date. Increasing the population of ECG big data annually requires automatic analysis and immediate interpretation for improved diagnosis. Modern AI techniques like deep learning (DL)-based convolutional neural networks (CNNs) provide an improved way of cardiac disease management and diagnosis. This review throws a light over application of AI in ECG analysis and its necessity. Rich sets of clinical ECG data curated carefully as private and public access developed for various cardiac and extra-cardiac diseases management. Rather than human ECG interpretation, AI can move modern medicine toward more personalized patient care. The intention of this review article is to assess clinical and research possibilities, gaps, and jeopardies involved in cardiac anomalies detection using ECG measurement.