Skip to content

Sparse Mixture of Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation

License

Notifications You must be signed in to change notification settings

SullyChen/SMoLK

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

13 Commits
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DOI

SMoLK

This repository contains the notebook responsible for training and testing the models in our paper, Learned Kernels for Sparse, Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation.

We have laid out a Jupyter Notebook containing the code and comments in chronological order, allowing one to train, test, and prune Learned Kernel Models as they appear in the paper. Please refer to the main Jupyter Notebook in this repository for the instructions and explanations surrounding this code — rather than providing a long readme to refer back to, we thought it would be easier to have a single blog-like guide from which one can walk through the experiments. Thank you!

Requirements

To run this code, you will need the following packages (version more recent than what is listed will likely work):

torch==2.1.1
scikit-learn==1.3.2
scipy==1.9.3
numpy==1.22.4
tqdm==4.66.1

Depending on your internet speed, this should only take around 20 minutes to install, though your times may vary depending on your machine. Machine-specific installation of PyTorch is recommended here

About

Sparse Mixture of Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation

Resources

License

Stars

Watchers

Forks

Packages

No packages published