epilepsy master-alpha

an open-source seizure predicting algorthim

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epilepsy master is an developing open-source system that predicts an upcoming seizure, and then prevents it by brain stimulating or using fast react drugs and ...
u can see the main project here:

in the phase alpha, we just focus on the predicting mechanism using continuous EEG data and developing a reliable algorithm for it.

the main idea is using realtime EEG data and using signal processing technics and include machine-learning to modify the algorithm for every single patient.

main signal processing technic that ever used for this systems is FFT beside the machine-learning.but moving average and ... can help to achieve reliable mechanism for seizure is a basic source about this issue:

lastly a competition had been held by kaggle to predict seizures by EEG data and there are several useful experiences that we can use them: and

now we are going to develop a reliable algorithm using available articles and other Science Team's experiences in matlab environment.

  • 1 × matlab for developing algorithms

  • Temporary victory

    amir.daaee09/26/2015 at 16:46 0 comments

    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:

    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.

    -Hjorth parameters: activity, mobility and complexity.

    -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.

  • let's start!

    amir.daaee09/20/2015 at 13:04 0 comments

    at the first I'm going to develop a simple code that using GLM method(by matlab lassoglm function) to predict an upcoming seizure. i think it's the most simple kind of machine learning that I'll use following feature that exported from EEG data on it:

    1. Spectrum and Shannon's entropy 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).

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

    3. Shannon's entropy at dyadic frequency bands.

    4. Spectrum correlation across channels at dyadic frequency bands.

    5. Time-series correlation matrix and its eigenvalues.

    6. Fractal dimensions.

    7. Hjorth parameters: activity, mobility and complexity.

    8. Statistical moments: skewness and kurtosis.

    I'll use 1 minute window length without overlapping area, but it may be changed by more studies.

    this features are used by winner team in kaggle challenge and I'll use them too, because at least we know there is a success experiment of them.

    i will use either forest tree and... after completing this scope :)

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ghazale1997s wrote 09/18/2015 at 14:40 point

good luck :)

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