Cardiac Axis Analysis using Machine Learning:...

Cardiac Axis Analysis using Machine Learning: A Survey

Author Name : Sunitha P, Abdul Riyan, Ahmed Rafiq Raazi, Akthar Zama, Armaan Salam

DOI: https://doi.org/10.56025/IJARESM.2025.1307250144

ABSTRACT Electrocardiogram (ECG) serves as a cornerstone in cardiac diagnostics. Its widespread use is attributed to its non invasiveness, cost-effectiveness, and reliability in capturing the heart’s electrical activity. A critical parameter derived from ECGs is the cardiac axis—a vector representation indicating the net direction of ventricular depolarization. Shifts in this axis can reveal underlying pathologies such as ventricular hypertrophy, conduction blocks, or other structural abnormalities. This literature survey delves into how machine learning (ML) and deep learning (DL) technologies are being leveraged to automate the detection and interpretation of the cardiac axis. We explore popular models, their comparative performance, real-time deployment possibilities, and how these tools can transform cardiac monitoring into a proactive and personalized health service.