The aim was to create a classifier that can detect if a patient has one of several types of heart condition, known as arrhythmia, given a time series of an electrocardiogram (ECG) heartbeat. We focused on self-supervised and transfer learning to see if we could improve the accuracy for two different network architectures.

The chosen approach involved first applying unsupervised clustering to the data, training a model using the assigned clusters as labels, and finally transferring the weight's of this model to another with the same architecture and training it to classify the original arrhythmia labels. For more information, see our project's site.

Our best model achieved a high test accuracy of 97% by combining a Convolutional Neural Network (CNN) layer with a Temporal Convolutional Network (TCN) as depicted in the diagram below. While unfortuantely our clustering transfer learning approach did not lead to a significant improvement in accuracy, our work goes to show that TCNs, a relatively new and underrepresented architecture for time-series data, results in superior performance compared to the current standard of CNN and LSTM architectures for ECG classification.

Download the ECG Heartbeat Categorization Dataset from Kaggle

1. Download the contents of this repository
2. Extract the contents of the zip
3. Open Google Colab
4. Upload the notebooks from the extracted zip