Deep ECG

Introduction

Build a deep neural network for ECG authentication

Low Complexity ECG Biometric Authentication for IoT Edge Devices

The whole project contains three different methods to rebuild the model and execute ECG authentication:

• Original CNN

  This rebuilds the model from a pre-trained CNN model and uses its original weights. The best performance is 99.63%.

• Binary Neural Network

  This rebuilds the model from a pre-trained CNN model and uses binary weights. The weights are in this format:

  

  The best performance is 88.85%

• Exponentiation Neural Network

  This rebuilds the model from a pre-trained CNN model and uses exponent weights. The weights are in this format:

  

  The performance increases with a larger 'n' value

Requirements and Installation

• Database

  PTB Diagnostic ECG Database

• Libraries

  Install the necessary libraries:

  conda env create -n deepecg --file environment.yml

  Activate environment:

  conda activate deepecg

Usage

• Step 1: Data Processing

  To process data from the original dataset manually, run the following:

  python DataGeneration.py \
      --data_path ${data_path} \
      --prefix ${prefix} \
      --output_path ${output_path}

  Or you can download the pre-processed dataset from the link:

  PTB Processed Dataset

• Step 2: CNN Modelling

  To build the CNN model for human recognition, run:

  python ModelCreation.py \
      --save_path ${save_path} \
      --data_path ${data_path}

• Step 3: Authentication

  To execute the authentication section, run the following:

  python Authentication.py \
      --model_path ${model_path} \
      --data_path ${data_path}

Update

• 2021/01/20

  optimize structure, improve performance, test generalization ability

• 2021/02/05

  optimize path search

• 2021/03/16 *

  update path structure

• 2021/05/10

  pruning