Kaggle Competition Goal

Detect seizure (preictal) or non-seizure (interical) segments of intracranial electroencephalography (iEEG) data. See Kaggle EEG Competition page for more details.

My Approach:

• Extract basic stats and FFT features for non-overlapping 30-second iEEG windows
• Detect signal drop out and impute missing data with mean for each feature per window
• Predict seizure and non-seizure segments using a stacked model.

Model Details

For more details about the model,  see my github repo with the documentation and R code.

Final Thoughts

• This is my first Kaggle competition. I acheived my goal of making a competition submission. See my profile.
• I submitted after the deadline but my submission would have ranked 391 of 2440 submissions. Screenshot
• The Kaggle model stacking tutorial helped me understand cross-fold validation with stacked models.
• Deep's Kernel and Tony Reina's Kernel helped me understand EEG features.
• Special thanks to Hassan Kingravi for suggesting the stacked model.