This repository contains the winning submission for UPenn and Mayo Clinic's Seizure Detection Challenge on Kaggle.

It has been updated as of 18 Feb 2018 to include a requirements.txt for installing dependencies via pip. Small changes were necessary to get the repo working again with updated libraries. These changes were tested using sample_clip.mat from the Kaggle competition data rather than the original data which I did not have on hand. For the original submission without these changes, you may checkout the tag original-submission, e.g. git checkout original-submission.

http://www.kaggle.com/c/seizure-detection

This README and repository modelled on https://www.kaggle.com/wiki/ModelSubmissionBestPractices

• 15" Retina MacBook Pro (Late 2013) 2.7GHz Core i7, 16GB RAM
• OS X Mavericks

• python 2.7 with virtualenv
• hickle==2.1.0
• numpy==1.14.0
• scikit-learn==0.19.1
• scipy==1.0.0

• pywt (for Daubechies wavelet)
• scikits talkbox (for MFCC)

Setup the virtualenv and install the dependencies

virtualenv venv
. venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt

Activate the virtualenv

. venv/bin/activate

Obtain the competition data and place it in the root directory of the project.

seizure-data/
  Dog_1/
    Dog_1_ictal_segment_1.mat
    Dog_1_ictal_segment_2.mat
    ...
    Dog_1_interictal_segment_1.mat
    Dog_1_interictal_segment_2.mat
    ...
    Dog_1_test_segment_1.mat
    Dog_1_test_segment_2.mat
    ...

  Dog_2/
  ...

The directory name of the data should match the value in SETTINGS.json under the key competition-data-dir.

Then simply run:

python -m train

One classifier is trained for each patient, and dumped to the data-cache directory.

data-cache/classifier_Dog_1_fft-with-time-freq-corr-1-48-r400-usf-gen1_rf3000mss1Bfrs0.pickle
data-cache/classifier_Dog_2_fft-with-time-freq-corr-1-48-r400-usf-gen1_rf3000mss1Bfrs0.pickle
...
data-cache/classifier_Patient_8_fft-with-time-freq-corr-1-48-r400-usf-gen1_rf3000mss1Bfrs0.pickle

Although using these classifiers outside the scope of this project is not very straightforward.

More convenient is to run the predict script.

python -m predict

This will take at least 2 hours. Feel free to update the classifier's n_jobs parameter in seizure_detection.py.

A submission file will be created under the directory specified by the submission-dir key in SETTINGS.json (default submissions/).

Predictions are made using the test segments found in the competition data directory. They are iterated over starting from 1 counting upwards until no file is found.

i.e.

seizure-data/
  Dog_1/
    Dog_1_test_segment_1.mat
    Dog_1_test_segment_2.mat
    ...
    Dog_1_test_segment_3181.mat

To make predictions on a new dataset, simply replace these test segments with new ones. The files must numbered sequentially starting from 1 otherwise it will not find all of the files.

This project uses a custom task system which caches task results to disk using hickle format and falling back to pickle. First a task's output will be checked if it is in the data cache on disk, and if not the task will be executed and the data cached.

See seizure/tasks.py for the custom tasks defined for this project. More specifically the MakePredictionsTask depends on TrainClassifierTask, which means predict.py will train and dump the classifiers as well as make predictions.

python -m cross_validation

Cross-validation set is obtained by splitting on entire seizures. For example if there are 4 seizures, 3 seizures are used for training and 1 is used for cross-validation.

{
  "competition-data-dir": "seizure-data",
  "data-cache-dir": "data-cache",
  "submission-dir": "./submissions"
}

• competition-data-dir: directory containing the downloaded competition data
• data-cache-dir: directory the task framework will store cached data
• submission-dir: directory submissions are written to

Available at https://github.com/MichaelHills/seizure-detection/raw/master/seizure-detection.pdf