Subangkar / Simsig

Source code of SimSig

SimSig

Source code & Pretrained models of SimSig, Contrastive Self-Supervised Learning Based Approach for Patient Similarity: A Case Study on Atrial Fibrillation Detection from PPG Signal
Two Pretrained pytorch model files for CPU is provided in saved_model folder. However, it will utilize GPU if available and the environment is set up. The simclr_ntxentmulti.pt is trained with NT-Xent Multi loss while the simclr_ntxent.pt is trained with NT-Xent loss.

Requirements

Python version 3.7+
PyTorch version 1.8+

How to run:

• First, set up a virtual environment and activate it
• Install all the requirements and their dependencies
• Then download the dataset, redistribute it and put that into proper file & folder structure
• Finally, run python Simsig.py (You can alternatively execute the notebook Simsig)

Data Folder Structure for running Simsig.py:

data/
    train/
        signal.npy
        rhythm.npy
        ids.npy
    test/
        signal.npy
        rhythm.npy
        ids.npy

Here, ids.npy is derived from corresponding parameters.npy for the set that contains individual id for the corresponding segment. The train set is required for generating the Patient Database .

Additional Files:

distr_split_ids.npy: A dictionary that contains list of individal ids for train, validation & test set for the redistribution of dataset according to BayesBeat: Reliable Atrial Fibrillation Detection from Noisy Photoplethysmography Data