This repository contains the code for the F-ML method described in the paper titled "A Spectral Machine Learning Approach to Derive Central Aortic Pressure Waveforms from a Brachial Cuff" by Alessio Tamborini, Arian Aghilinejad and Morteza Gharib.

The F-ML method is a spectral machine learning approach designed to reconstruct a target pressure waveform from a input pressure waveform measurement where these waveforms are contained within the same system. The referenced paper demonstrates this methodology by transferring a brachial pulse waveform to an aortic waveform. However, the versatility of this method allows for transferring waveforms between any two locations.

For installation, just clone this repository and install locally:

git clone https://github.com/aletambo/FML-TransferFunction.git
cd FML-TransferFunction
pip install -e .
pip install -r requirements.txt

After you have installed the library, you can start training the F-ML Transfer Function method as shown below:

from FML import FML

# Initialize the F-ML Transfer Function
fml = FML(
    input_modes=20,
    target_modes=20,
    name='F-ML Transfer Function', 
    samp_freq=1000
)

# Train the model with input and target data
fml.train(inputs, targets)

We welcome contributions to the project! Please follow these guidelines when contributing:

1. Reporting Issues: If you encounter any issues, please report them using our issue tracker.

2. Submitting Pull Requests: If you would like to contribute code, please fork the repository and create a new branch for your feature or bug fix. Once your changes are ready, submit a pull request for review.

Thank you for your contributions!

If you use this code in your research, please cite the following paper:

@article{tamborini2023spectral,
    title={A Spectral Machine Learning Approach to Derive Central Aortic Pressure Waveforms from a Brachial Cuff},
    author={Alessio Tamborini and Arian Aghilinejad and Morteza Gharib},
    journal={TBD},
    year={TBD},
}

For any questions or inquiries, please contact:

• Alessio Tamborini: [email protected]