- model.py coontains the neural network used to train the data
- explorer.py contains the code used to identify the types of ECG for each patient
- datapipeline.py contains the code used to extract data from .mat files and convert it into csv file with categorical coloumns
- data2.csv contains the final dataset used for training
Cardio Vascular Diseases happens to be the major contributor of death rate. Heartbeat is a basic physiological function of the human body and it indicates and helps a lot in investigation of heart function. One non-invasive method of assessing heart function is using an ECG. The dataset provided for this challenge has 17 classes of ECGs. There are attempts made classifying this data using different approaches, please refer online for sources. There is one article which is provided in the references section for you to understand the problem better
To propose and implement an intelligent real time heartbeat classification algorithm and supplement results with Explainable AI. You are expected to come out with an approach to solve the classification problem as a real-time solution (so it could be fabricated into wearables) and embed ‘Explainable AI’ to substantiate your classification semantically
