Deep Learning-Based Near-Lossless Compression of 12-Lead ECG Images for Efficient Telemedicine Data Delivery

A deep learning-based near-lossless compression of 12-lead ECG images for efficient telemedicine data delivery.

A deep learning-based near-lossless compression of 12-lead ECG images for efficient telemedicine data delivery. It is offered as a reference project: a complete, worked example of how a system like this is designed, built and evaluated, so that a developer can study it, run it and learn from it.

It suits learners who want to work with signals and wireless data: acquiring or simulating signals, filtering and extracting features, and evaluating the results.

The main parts are real-time processing of incoming data, signal acquisition or simulation, filtering and feature extraction, a processing or learning model, and plots and a clear evaluation. The reference build uses TensorFlow, Python, NumPy, SciPy, and MATLAB, with a clear project structure, setup instructions and sample data so that it can be run and extended.