Authors: Rastko Stojsin, Teddy Weaver
2020 has been a stressful year for just about everyone. How's your heart doing?...... Are you sure?
To help you find out, we've developed a kinda-sorta accurate heartbeat classifier to determine if your heartbeat is regular (yay!), has a murmur, exhtole, or just too much background noise to tell the difference. All you need is your smartphone!
Armed with ~500 heartbeat audio files of questionable quality on Kaggle, we transformed the audio files into different visual representations of frequency and pitch.
To create our mediocre multi-class classifier, we used transfer learning of a Convolutional Neural Network (CNN) -- a class of deep neural networks that while originally designed for image classification. The base architecture we used for each of our ensemble models was a ResNeXt-101-32x8d.
The result -- Around 72% accuracy in predicting a heartbeat!
--- Next Steps ----
- Learn better memory management in PyTorch! We ran out of RAM very quickly with higher DPI images
- Research audio signals to create better features or images
- Try other models! Would a RNN work better?
- Host models with a web service. Currently being run as part of the python scripts
- Take a nap