AI driven ECG arrhythmia diagnosis

Abstract

The accurate and timely diagnosis of cardiac arrhythmias is crucial for effective patient management and improved health outcomes. However, the precise identification of arrhythmias in electrocardiogram (ECG) data often requires specialized medical expertise, leading to potential delays and errors in diagnosis. To address these challenges, this project introduces an AI-driven system for ECG arrhythmia diagnosis. Employing advanced deep learning techniques, the proposed system leverages a comprehensive dataset of annotated ECG recordings to train a robust model capable of detecting and classifying various types of arrhythmias. The model is designed to process raw ECG signals, extract relevant features, and generate clinically meaningful insights, enabling automated and rapid identification of arrhythmic patterns. Through a user-friendly interface, medical professionals can upload ECG data for real-time analysis, allowing for prompt decision-making and personalized patient care. Furthermore, the system offers interpretable results, highlighting key indicators and providing detailed explanations to aid clinicians in understanding the diagnostic outcomes.

Keywords

• ecg
• machine learning
• deep learning
• concurrent neural network
• ecg
• signals
• arrhythmia.