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https://github.com/truongnmt/deepecg
Using deep learning to detect Atrial fibrillation
https://github.com/truongnmt/deepecg
atrial-fibrillation cardio csv dataset deep-learning ecg ecg-qrs-detection electrocardiogram gnuplot ipynb keras mat neural-network python rhythm shell signal tensorflow wfdb
Last synced: 4 months ago
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Using deep learning to detect Atrial fibrillation
- Host: GitHub
- URL: https://github.com/truongnmt/deepecg
- Owner: truongnmt
- License: mit
- Created: 2018-04-30T11:08:10.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2021-12-15T03:10:03.000Z (about 3 years ago)
- Last Synced: 2024-09-27T23:41:03.363Z (4 months ago)
- Topics: atrial-fibrillation, cardio, csv, dataset, deep-learning, ecg, ecg-qrs-detection, electrocardiogram, gnuplot, ipynb, keras, mat, neural-network, python, rhythm, shell, signal, tensorflow, wfdb
- Language: Jupyter Notebook
- Size: 1.12 MB
- Stars: 31
- Watchers: 4
- Forks: 13
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
README
# DeepECG
## Basic Overview
Atrial Fibrillation detection with a deep probabilistic model. Backend for [Diagnose Report ](https://github.com/truongnmt/diagnose-report) app. Use [PhysioNet dataset](https://physionet.org/challenge/2017/) for model training and testing.
## Dependencies
The following dependencies are required.
* Python
* [Tensorflow](https://www.tensorflow.org/install/)
* [Keras](https://keras.io/)
* [Cardio Framework](https://github.com/analysiscenter/cardio)
## Demo
Demo from the frontend: [Diagnose Report ](https://github.com/truongnmt/diagnose-report) app.
|Sign Up|Dashboard|Report detail| Create report|
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## Usage
I have already ran training for you. You can use the saved model in `dirichlet_model` folder to predict right away.
But make sure to change the path in `direchlet_model/checkpoint` according to your path.
If you want to train again by yourself, run this following notebook file: [dirichlet_model_training.ipynb](https://github.com/truongnmt/DeepECG/blob/master/dirichlet_model_training.ipynb), download the [dataset](https://physionet.org/challenge/2017/) and start training. Notice that on 1000 epochs, the training will take some time. Mine took about 8-9 hours on Tesla K80.
For the project I'm working on, I create some shell file and python files to convert and predict stuffs.
* To predict whether an image is AF (Atrial Fibrillation) or not:
```shell
predict
```
It will return something like this
```js
[{'target_pred': {'A': 0.021675685, 'NO': 0.9783243},
'uncertainty': 0.0073926448822021484}]
```
Which `A` is – Atrial fibrillation
`N` – Normal rhythm, `O` – Other rhythm, so `NO` is no problem.
See more in [test.ipynb](https://github.com/truongnmt/DeepECG/blob/master/test.ipynb) for more test case and example.
* To generate image from csv:
```shell
gnuplot -e "fileIn='csv/04015.csv'; fileOut='uploads/04015.png'" csv2img.gnuplot
```
* To convert single file to csv and image:
```shell
./raw2img
```
* To convert image to csv:
```shell
python img2csv.py ''
```
* To convert MAT to csv:
```shell
python mat2csv.py "raw/A00001.mat"
```
* To convert csv to signal, with Gain equal 1000, Frequency 300Hz. Notice that you can specify `-f` mean from which line (remove if from beginning of file) and also `-t` mean to which line.
```shell
wrsamp -i raw/A00001.csv -o raw/A00001-converted -G 1000 -F 300 -z
```
* To convert signal back to MAT:
```shell
wfdb2mat -r raw/A00001-converted
```
## License
This project is licensed under the terms of the **MIT** license.