The objective of this work is to detect false arrhythmia alarms using convolutional neural networks as proposed in Reducing False Arrhythmia Alarms in the ICU: the PhysioNet/Computing in Cardiology Challenge 2015. This project was the code from my undergraduate thesis to finish my computer engineering degree.
├── LICENSE
├── README.md <- This file
├── reports
| └── figures <- Figures used in the final report
| └── results <- Tables with results of the 3-step grid-search training
│
├── requirements.txt <- pip requirements
├── requirements-conda.txt <- conda requirements
│
├── src
│ ├── data
│ │ ├── make_dataset.py <- code to download, create and prepare database
│ │ ├── plot_dataset.py <- auxiliar functions to create the plots used as input
│ │ ├── prepare_dataset.py <- auxiliar code to prepare dataset
│ │ └── resample_dataset.py <- random oversampling code
│ │
│ ├── features
│ │ └── build_features.py <- bottleneck features generation
│ │
│ ├── models
│ │ ├── metrics_callback.py <- class for using as callback on keras models
│ │ ├── models.py <- models used in training
│ │ └── train_model.py <- code for model training
│ └── experiment.py <- experiment run
Project based on the cookiecutter data science project template. #cookiecutterdatascience