rohannaidu / sp-2016

Kaggle Seizure Prediction Competition

Description

Seizure Prediction

• Competition page at Kaggle
• This is a proof-of-concept for applying deep learning techniques to EEG data converted into spectrograms.
• This code builds a separate model for each subject (there are three subjects) and each electrode.
• Predictions based on all electrodes are averaged to arrive at the final prediction.

Usage

These steps take about 4 hours on a system with 4 processors, a single GPU and a spinning hard-disk. Tested only on Ubuntu.

1. Download and install neon 1.5.4

   git clone https://github.com/NervanaSystems/neon.git
   cd neon
   git checkout v1.5.4
   make
   source .venv/bin/activate
   

2. Verify neon installation

   Make sure that this command does not result in any errors:

   ./examples/cifar10_msra.py -e1
   

3. Install prerequisites

   pip install scipy sklearn scikits.audiolab
   

4. Download the data files from Kaggle:

   Save all files to a directory (referred to as /path/to/data below) and unzip the .zip files.

5. Clone this repository

   git clone https://github.com/anlthms/sp-2016.git
   cd sp-2016
   

6. Train models and generate predictions

   ./run.sh /path/to/data /path/to/output 2>&1 | tee run.log
   

   where /path/to/data must contain the data subdirectories (train_1, train_2 etc.) as well as sample_submission.csv and /path/to/output is a new directory that will be created to store intermediate output files.

7. Evaluate predictions

   Submit subm.csv to Kaggle

Notes

• The model requires 3GB of device memory.
• If using AWS, see slide 10 on [this deck] (https://github.com/anlthms/meetup2/blob/master/audio-pattern-recognition.pdf) for instructions on how to configure an EC2 instance.
• The first run takes longer due to conversion of .mat files into .wav files.
• Conversion of data to spectrograms is performed on the fly by neon.
• A leaderboard AUC score of 0.64 may be obtained by using this code as is.