Autentificate_ECG

Using neural networks on ECG data to see if it is a valid candidate for biometric authentication.

Pre-Requisites

Make sure you have the following items installed:

1. python2.7
2. wfdb package using pip install wfdb
3. keras
4. pandas
5. numpy

Usage

1. python data_processing.py: Converts all .dat files in data/ to .csv. Extract labels and features from individual .csv files. Outputs the following files in the processed_data/ folder:

   • labels.csv: labels [person_id, recording_label, signal_id, age, gender, date record was collected]
   • rec_##.csv: filtered ecg signals & unfiltered ecg signals [noisy]

2. python model_personid.py: Train and evaluate model for person identification. See line 370 in data_processing.py on specific instructions for data setup.

Database

This project uses the ECG-ID Database from Physionet. It can be found in the data/ folder. [Note: the original database does not include .csv files. See class Generate_csv in data_processing.py to learn more about how the .dat files were converted to csv using rdsamp]