# Event based gQRS

This is the official repository for the publication: Silvio Zanoli, Tomas Teijeiro, Fabio Montagna and David Atienza: "An Event-Based System for Low-Power ECG QRS
Detection"

In this repository is possible to find the source code for the event-based gQRS algorithm for QRS complex detection in ECG signals. 

The python demonstrative version, C version implemented on the PULP platform, Mr.Wolf, and the tools necessaries to extract and re-sample the data  can be found here.

## Getting Started

### Prerequisites

### Python version:

- Jupyter-notebook

- Python 3.6.x*

- Numpy

- Pandas

- Matplotlib

- wfdb

- tqdm


### C version:

- Python 3.5.3*

- gCC
 
- [PULP platform SDK and toolchain](https://github.com/pulp-platform/pulp-sdk)


\* Note: the Pulp SDK is built to work within Ubuntu 16.04 with the default python version being: 3.5.3. The python version this code was designed with (the Python part) is python 3.6.x and tested on python 3.7.1. 
Our suggestion is to have a Ubuntu 16.04 machine with default python3 -> python3.5.3 and a second python 3.6.x installation used to execute exclusively this code. 
Alternatively, it is possible to run the C Version on an ubuntu 16.04 virtual machine once all the data and the python calls have been executed on a Linux OS with a python version 3.6.x or higher (tested on 3.7.1)

## Installing and Running

- Create a data folder on the root direcotry of this project, this will be the directory of your RAW data and the output directory.

- Insert your dataset in the MIT-BIH database format (.dat and .hea files) in a subfolder, for example, "dataRaw".

- The database used for developing and testing this algorithm can be found at [MIT-BIH Arrythmia Database](https://physionet.org/content/mitdb/1.0.0/). NOTE: The code 
given here is designed to work specifically with this database

- Move in the "data_parsing" folder and lunch ECG_lvlCrossing.py

```
python3 ECG_lvlCrossing.py -i ../data/dataRaw -o ../data/dataOut -b 5 [-l 'MLII'][--hist 0]
```
This script will extract and resample with an emulated level crossing ADC with number of bits "-b" all the file in the MIT-BIH format found in the direcotry passed by the "-i" argument and output the results in the folder passed with "-o", in the subfolder "dataOut/lvlCrossingBits__Hist__". The default selected electrode is designated as 'MLII', it is possible to change the used electrode with the argument "-l 'Electrode Name'". By default no Hysteresis is used around the threshold levels. it is possible to give a histeresis value (in percentage) with the argument '--hist'

(Note: is possible also to pass the path of a single file as input.)


### Python version:

- Run the jupiter notebook contained in the "python_gQRS" folder. This notebook will produce, at the end, a "result" folder (../data/results) in wich is possible to find the results (accuracy)


### C version:

Important note: This is not a plain C-99 project. This project was developed for the PULP platform "Mr. Wolf". The C code check only one file

- Move in the "./c_gQRS" folder and lunch "makeDataHeader.py"

```
python3 makeDataHeader.py -f ../data/dataOut/[desired re-sampled folde]/[desired File].csv -d [Directory containing the atribute file for the target file] [ -a "Atribute extension" ]
```
This will create automatically the header containing the data from file "selectedFile" to be used by the C code (data.h in ./c_gQRS)

- Move to the "./c_gQRS" folder

- follow the steps at [PULP platform SDK and toolchain](https://github.com/pulp-platform/pulp-sdk).

- Execute:

```
make clean all run
```
This will start to run the simulation for the selected file multiple time, measuring several parameters of the simulation and returning an overview of the score-results and the performance-results 

## Built With

* [Anaconda](https://www.anaconda.com/) - The iPython framework used
* [PULP SDK](https://github.com/pulp-platform/pulp-sdk) - SDK for the PULP platform

## Authors

* **Silvio Zanoli**  - [ESL Lab](https://www.epfl.ch/labs/esl/)
* **Tomas Teijeiro** - [ESL Lab](https://www.epfl.ch/labs/esl/)
* **Fabio Montagna** - [University of Bologna](https://www.unibo.it/en)

## License

This project is licensed under the GPL License - see the <u>LICENSE.txt</u> file for details

## Acknowledgments

Thanks to:

* Prof. Dr. David Atienza Alonso 

* The Human Brain Project (HBP) SGA2 (GA No. 785907)

* The DeepHealth Project (GA No. 825111)

* The SNF through the ML-Edge Project (GA No. 182009)

* The [ESL Lab](https://www.epfl.ch/labs/esl/)