This repository is an attempt to solve the American Epilepsy Society Seizure Prediction Challenge on Kaggle. This repository started of from the winning submission for UPenn and Mayo Clinic's Seizure Detection Challenge on Kaggle.

An attempt was made to keep the two tasks (detection and prediction) continue to work with this repository. You can select which task (prediction or detection) is being carried using

  "task": "predict",

inside SETTINGS.json (the defaut is detect.) This flag also perform the necessary corrections necessary to handle the differences in the file formats used in the two competions.

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

##Hardware / OS platform used

• 15" Retina MacBook Pro (Mid 2012) 2.6GHz Core i7, 16GB RAM
• OS X 10.9

##Dependencies

###Required

• Python 2.7
• scikit_learn-0.14.1
• numpy-1.8.1
• pandas-0.14.0
• scipy
• hickle (plus h5py and hdf5, see https://github.com/telegraphic/hickle for installation details)

###Optional (to try out various data transforms)

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

##Train the model and make predictions

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

seizure-data/
  Dog_1/
    Dog_1_preictal_segment_0001.mat
    Dog_1_preictal_segment_0002.mat
    ...
    Dog_1_interictal_segment_0001.mat
    Dog_1_interictal_segment_0002.mat
    ...
    Dog_1_test_segment_0001.mat
    Dog_1_test_segment_0002.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:

./train.py

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.

./predict.py

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_0001.mat
    Dog_1_test_segment_0002.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.

./cross_validation.py

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.

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

• task: can have the value predict, any other value will change the task to detection
• 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

The original detection model is available at https://github.com/MichaelHills/seizure-detection/raw/master/seizure-detection.pdf