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BayesBeat

Source code & Pretrained model for our IMWUT (UbiComp) 2022 paper: "BayesBeat: A Bayesian Deep Learning Approach for Atrial Fibrillation Detection from Noisy Photoplethysmography (PPG) Signals" [preprint]

The Pretrained pytorch model file for CPU is provided in saved_model folder

Requirements

CUDA version 10.2+
Python version 3.7+
PyTorch version 1.5.1+

How to run:

  • First, setup a virtual environment and activate it
  • Install all the requirements and their dependencies
  • Then download the dataset and put that into proper folder structure
  • Finally, run python evaluate_bayesian.py
  • For the version that utilizes gpu to evaluate, please refer to this repo: https://github.com/Subangkar/BayesBeat

Data Folder Structure for running evaluate_bayesian.py:

data/
    test/
        signal.npy
        qa_label.npy
        rhythm.npy

Additional Files:

distr_split_ids.npy: A dictionary that contains list of individal ids for train, validation & test set for the distribution of dataset

Citation

If you use our work, please cite:

@article{das2022bayesbeat,
  title={BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data},
  author={Das, Sarkar Snigdha Sarathi and Shanto, Subangkar Karmaker and Rahman, Masum and Islam, Md Saiful and Rahman, Atif Hasan and Masud, Mohammad M and Ali, Mohammed Eunus},
  journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume={6},
  number={1},
  pages={1--21},
  year={2022},
  publisher={ACM New York, NY, USA}
}