1. Explainable Electrocardiogram Classifications using Neural Networks
Group 5
Ryan Roggenkemper, Harikaran Subbaraj, Julianna White, Mujahid Zaman

2. ECG Classification ECG: measurements of heart activity over time
Historically analyzed by clinicians to diagnose problems/diseases
Recently, attempts to use machine learning to classify, help clinicians make decisions
Problems:
Some classes/diseases are very rare, hard to train models for
Classes/diseases aren’t necessarily mutually exclusive

3. Attention Models (and how they might help)
Attention modeling has been used in a variety of areas to encourage deep learning architectures to focus on a particular part of the input when making predictions.
E.g. looking at contextual words when translating a particular word in machine translation, or concentrating on specific areas of an image during caption generation.
A side benefit of Attention modeling is that it allows for visualization/analysis of what a neural network is “paying attention” to.
We hope to use this fact to learn more about what previous ECG classifiers are doing wrong when attempting to classify rare/overlapping diseases.