This repository contains the code of pre processing and deep learning model that was employed in our work: A Hybrid Deep Morpho-Temporal Framework for Oscillometric Blood Pressure Measurement
1 - The datasets subdirectories should be created as follows: *root directory/OMW1 *root directory/CP_LPf *root directory/indf *root directory/denoise
2 - Run the Matlab code, which is included in the dataset folder, to prepare to generate files.
3 - Using the 'DeepOscillometry.yml' file and Anaconda, install all the required packages: conda env create -f DeepOscillometryEnv.yml
4 - The environment should be activated.
5 - Open the 'Pre Processing.py' file and run the preprocess function first, then the single_interpolation function.
6 - Save the preprocessed fie in defined direction.
7 - Run "Model.py" to train the network.
8 - Run "Test.py".