Transformer Encoder Driven Deep Learning Network for Single Lead ECG Arrhythmia’s
Objective
Develop a robust deep learning system for single-lead ECG arrhythmia classification using FREQ-ECG-Net and QuadPillar models with preprocessed ECG images. Implement a Flask backend and interactive front-end to provide explainable, scalable arrhythmia predictions with high accuracy and user-friendly visualizations. Continuously evaluate performance using standard metrics to ensure reliable, interpretable, and clinically useful detection across multiple arrhythmia types.
Specifications
SOFTWARE REQUIREMENS
Operating System : Windows 7/8/10
Server side Script : HTML, CSS & JS
Programming Language : Python
Libraries : scikit-learn, pandas, numpy, matplotlib, seaborn, TensorFlow, Keras, Flask, SQLAlchemy.
IDE/Workbench : VSCode
Server Deployment : MYSQL
Database : MySQL
HARDWARE REQUIREMENTS
Processor - I3/Intel Processor
RAM - 8GB (min)
Hard Disk - 128 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - Any
Paper Publishing
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