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A statistical framework for EEG channel selection and seizure prediction on mobile

  • Fatma IbrahimEmail author
  • Saly Abd-Elateif El-Gindy
  • Sami M. El-Dolil
  • Adel S. El-Fishawy
  • El-Sayed M. El-Rabaie
  • Moawaed I. Dessouky
  • Ibrahim M. Eldokany
  • Turky N. Alotaiby
  • Saleh A. Alshebeili
  • Fathi E. Abd El-Samie
Article
  • 34 Downloads

Abstract

This paper presents a patient-specific approach for electroencephalography (EEG) channel selection and seizure prediction based on statistical probability distributions of the EEG signals. This approach has two main phases; training and testing phases. In the training phase, few hours of multi-channel nature for each patient representing normal, pre-ictal, and ictal activities are selected. These hours are segmented into non-overlapping 10-s segments and probability density functions (PDFs) are estimated for the signals, their derivatives, local means, local variances, and medians. These PDFs have multiple bins, which are studied separately as random variables across different segments of the same nature. Depending on the PDFs of these random variables for different signal activities and on predefined prediction and false-alarm probability thresholds, bins are selected from certain channel distributions for seizure prediction. In the testing phase, the selected bins only are used for classification of each signal segment activity into pre-ictal or normal states in the prediction process. In the final prediction step, an equal gain decision fusion process is performed leading to a discrete decision sequence representing the activities of all segments. This sequence is filtered with a moving average filter and compared to a patient-specific prediction threshold. Moreover, we have studied the effect of a lossy compression technique on the accuracy of the proposed algorithm using discrete sine transform (DST) compression. This system can be implemented for communication between headset and mobile to give alerts for patients.

Keywords

EEG Seizure prediction Channel selection Statistical analysis DST compression 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Fatma Ibrahim
    • 1
    Email author
  • Saly Abd-Elateif El-Gindy
    • 1
  • Sami M. El-Dolil
    • 1
  • Adel S. El-Fishawy
    • 1
  • El-Sayed M. El-Rabaie
    • 1
  • Moawaed I. Dessouky
    • 1
  • Ibrahim M. Eldokany
    • 1
  • Turky N. Alotaiby
    • 2
  • Saleh A. Alshebeili
    • 3
    • 4
  • Fathi E. Abd El-Samie
    • 1
  1. 1.Department of Electronics and Electrical Communications Engineering, Faculty of Electronic EngineeringMenoufia UniversityMenoufEgypt
  2. 2.KACSTRiyadhKingdom of Saudi Arabia
  3. 3.Electrical Engineering Department, KACST-TIC in Radio Frequency and Photonics for the e-Society (RFTONICS)King Saud UniversityRiyadhKingdom of Saudi Arabia
  4. 4.Department of Electrical EngineeringKing Saud UniversityRiyadhSaudi Arabia

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