at last i find the QMSDP team algorithm for seizure predicting efficient enough.

I'm going to start using it's Q features part in real device temporary and after a success running the algorithm on electrical device, I'll add other QMSDP parts to it.

you can see whole project here: https://github.com/drewabbot/kaggle-seizure-prediction

i can tell a summary of Q features algorithm as following:

using lasso GLM mechanism on following functions:

-Spectrum at six frequency bands: delta (0.1-4Hz), theta (4-8Hz), alpha (8-12Hz), beta (12-30Hz), low-gamma.

(30-70Hz) and high gamma (70-180Hz).

-Spectral edge power of 50% power up to 40Hz.

-Shannon's entropy at dyadic frequency bands.

-Spectrum correlation across channels at dyadic frequency bands.

-Statistical moments: skewness and kurtosis.

this part uses 1 minute windows and 400hz sampling rate.

it will collapses the scores to a single score by mean function.