All the notebooks, files, and data regarding the tasks are present in this git repository.
For the first task is to store data in a smart way and get the required data quickly.
I have implemented a Streamlit app where filtering data to get the required data immediately is just a click away.
The following operations have been implemented:
1) Add new data with heart images and signal data. The heart images and signal data are organized and the file paths are stored in the PostgreSQL database.
2) Filter data on any column. Data can be filtered based on Age, Gender, Health Conditions, Hospital, and the respective unit of the hospital.
Here is a short video of the demonstration:
The video is on YouTube! Note - File size was large.
For Task 2, I have worked with the ECG Fragments dataset and have built a classifier using CNN for classifying between fragments that contain a Ventricular Flutter or Fibrillation and the ones that do not.
The notebook is available here.
The accuracy achieved by CNN on Test data is 94.48%. This accuracy was achieved by saving the best model using training where I used the early stopping method to save the best model. The training and validation graphs are shown below:
The Classification report on the Test Data is as follows:
Here 1 is VFL/VF, 0 is NOT VFL/VF
The below are the Accuracy, Precision, Recall, F1-score, and ROC-AUC results obtained from the model:
I have also implemented the same model using the K-Fold Cross Validation technique which is used to find the best-performing model by iteratively working on a portion of the data. This helps to overcome the problem of Overfitting and also assess and identify the best-performing model.
The results are as follows:
Number of times K-Fold is done: 5
Number of Best Models saved: 5
The accuracy of the 5 models using Test data: [0.86614, 0.94488, 0.89763, 0.91338, 0.92125]
The mean accuracy of the 5 models is: 0.90866