CNN Based Arrhythmia Classification Using ECG Image Datasets

Abstract

The purpose of the project’s system to automate and optimize the process of diagnosing cardiac arrhythmias from ECG data, transitioning from manual evaluation to a robust and scalable computerized solution. The system ensures that critical data is stored securely and can be accessed, manipulated, and utilized for long-term medical analysis, assisting healthcare providers in improving patient outcomes and operational efficiency. The system architecture involves modules for preprocessing ECG data, converting it into image representations, and utilizing CNN models for classification. By automating the classification process, the system minimizes human intervention and errors, ensuring consistent and high- quality results. Furthermore, it supports seamless data retrieval and avoids redundant entries, promoting better resource utilization in clinical settings. In essence, this project focuses on achieving high accuracy in arrhythmia detection while ensuring usability and performance. It represents as our significant step toward integrating advanced AI techniques into healthcare for better service delivery.