BP-Net

The repository contains code for the paper titled, "BP-Net: Efficient Deep Learning for Continuous Arterial Blood Pressure Estimation using Photoplethysmogram", which has been accepted at the (20th IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS).

Files description

• dataloader.py
  Contains required dataloader files
  
• model.py
  Contains model architecture
  
• SSL.py
  Training script for Self-Supervised Learning
  
• train.py
  Driver code for training the model
  
• test.py
  Test script for model inference
  
• eval.py
  Evaluation script for evaluating a particular model checkpoint
  
• edge_port.py
  Script for porting torch model to ONNX format
  
• edge_eval.py
  Evaluation script for evaluating on device with ONNX Runtime library
  

Note

• Download the required data and model files from drive.
• The data files are obtained from (PPG2ABP) and best fold data is available at Data.
• After 10 fold cross validation training, the best model is available at model.pt.

Publication Link

http://arxiv.org/abs/2111.14558