# ECG-Synthesis-and-Classification
**Repository Path**: gzupanda/ECG-Synthesis-and-Classification
## Basic Information
- **Project Name**: ECG-Synthesis-and-Classification
- **Description**: 1D GAN for ECG Synthesis and 3 models: CNN, LSTM, and Attention mechanism for ECG Classification.
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 4
- **Forks**: 0
- **Created**: 2021-11-02
- **Last Updated**: 2025-08-13
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# 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](https://www.kaggle.com/shayanfazeli/heartbeat).
## 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](https://www.kaggle.com/polomarco/1d-gan-for-ecg-synthesis) and here - [ECG Classification | CNN LSTM Attention mechanism](https://www.kaggle.com/polomarco/ecg-classification-cnn-lstm-attention-mechanism).
### Models
## GAN Results
## Classification Results
