ECG-Synthesis-and-Classification

1D GAN for ECG Synthesis and 3 models: CNN with skip-connections, CNN with LSTM, and CNN with LSTM and Attention mechanism for ECG Classification.

Motivation

ECG is widely used by cardiologists and medical practitioners for monitoring the cardiac health. The main problem
with manual analysis of ECG signals, similar to many other
time-series data, lies in difficulty of detecting and categorizing
different waveforms and morphologies in the signal. For a
human, this task is both extensively time-consuming and prone
to errors. Let's try to apply machine learning for this task.

Data

Available here.

Formulation of the problem:

Each signal should be labeled as one of the classes ("Normal", "Artial Premature", "Premature ventricular contraction","Fusion of ventricular and normal", "Fusion of paced and normal").

Solution

Code with research and solution is available here - 1D GAN for ECG Synthesis and here - ECG Classification | CNN LSTM Attention mechanism.

Models

GAN Results

Classification Results