Automated Real-time Detection and Prediction ...

Automated Real-time Detection and Prediction of Electrocardiogram abnormalities Using Data Analytics

Author Name : Dr. D K Ravish, Dr. Krishna Prasad K

DOI: https://doi.org/10.56025/IJARESM.2024.121124077

ABSTRACT This study presents a novel approach for automated real-time detection and prediction of electrocardiogram abnormalities using data analytics. The proposed system leverages advanced machine learning algorithms to analyze ECG signals in real-time and detect abnormalities such as arrhythmias, ischemia, and heart blocks. By continuously monitoring ECG data, the system can provide early warning of potential cardiac events and improve patient outcomes. This proposed research focuses on the development and implementation of an automated system for real-time detection The study utilizes advanced machine learning techniques to analyze ECG data streams and promptly detect deviations from normal cardiac rhythms. By leveraging a robust dataset of annotated ECG recordings, the proposed system aims to enhance diagnostic accuracy and timeliness in identifying various cardiac conditions, including arrhythmias and ischemic events. The effectiveness of the automated prediction model is evaluated through comprehensive performance metrics, demonstrating its potential to support clinicians in early intervention and improved patient care outcomes.