diff --git a/README.md b/README.md
index 8af7230..f682a1d 100644
--- a/README.md
+++ b/README.md
@@ -1,103 +1,111 @@
 # Event based gQRS
 
-In this repository is possible to find the source code for the event-based gQRS algorithm for R peak detection in ECG signals. 
+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:
 
 - gCC
  
 - [PULP platform SDK and toolchain](https://github.com/pulp-platform/pulp-sdk)
 
 
 ## 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/)
 
 - Move in the "data_parsing" folder and lunch ECG_lvlCrossing.py
 
 ```
 python3 ECG_lvlCrossing.py -i ../data/dataRaw -o ../data/dataOut --threshold 100
 ```
 This script will extract and resample with a threshold "--threshold" 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"
 
 (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.
 
 
 ### C version:
 
 Important note: This is not a plain C-99 project. this project was developed for the PULP platform "Mr. Wolf"
 
 - Move in the "data_parsing" folder and lunch "makeDataHeader.py"
 
 ```
 python3 makeDataHeader.py -s ../data/dataOut/selectedFile
 ```
 This will create automatically the header containing the data from file "selectedFile" to be used by the C code
 
 - 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/)