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Efficient frameworks for statistical seizure detection and prediction

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Abstract

This paper presents two efficient frameworks for seizure detection and prediction that depend on statistical analysis. The common thread between them is the selection of certain attributes extracted from the electroencephalography (EEG) signals and the derivation of probability density functions (PDFs) of these attributes in two different types of activities of EEG signals. The first framework is for seizure detection based on scale-invariant feature transform (SIFT). Its idea is to choose some segments for normal and seizure activities. These segments are transformed to 2D matrices to be treated with the well-known SIFT. The feature key-points are extracted from those 2D matrices. The parameter that is used to discriminate between normal and seizure activities is the number of key-points. The PDFs of the number of key-points in cases of normal and seizure activities are estimated and a threshold value is used to classify any new segment as either a seizure or a normal segment. The other framework is for seizure prediction. The discrimination in this case is between normal and pre-ictal activities. A statistical treatment is performed on five attributes extracted from the wavelet transforms of different EEG segments for normal and pre-ictal activities. These attributes are amplitude, local mean, local variance, median and derivative. All PDFs of these attributes are estimated for normal and pre-ictal activities. Thresholds are set for all attributes. Decisions are taken based on these thresholds. Finally, a majority-voting strategy is used to merge decisions taken for all attributes.

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All data are available upon request from the corresponding author.

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Acknowledgements

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Funding

The authors did not receive support from any organization for the submitted work.

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Authors and Affiliations

  1. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Minia University, Minia, 61111, Egypt

    Ali Ahmed Khalil & Ashraf A. M. Khalaf

  2. Information and Digital Transformation Department, Institute of Public Administration, Riyadh, Saudi Arabia

    Ali Ahmed Khalil

  3. Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt

    Mostafa I. El Sayeid, Fatma E. Ibrahim, Walid El-Shafai, El-Sayed M. El-Rabaie, Adel S. El-Fishawy, Moawad I. Dessouky, Ibrahim M. El-Dokany & Fathi E. Abd El-Samie

  4. Department of Computer Engineering, Computer and Information Technology College, Taif University, Taif, Saudi Arabia

    Entessar Gemeay

  5. Department of Electronics and Electrical Communications Engineering, Faculty of Engineering, Tanta University, Tanta, Egypt

    Entessar Gemeay, Hossam Kasem & Salah Eldeen A. Khamis

  6. Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufa University, Menouf, 32952, Egypt

    Ghada M. El-Banby

  7. Security Engineering Lab, Computer Science Department, Prince Sultan University, Riyadh, 11586, Saudi Arabia

    Walid El-Shafai

  8. KACST, Riyadh, 11442, Kingdom of Saudi Arabia

    Turkey Alotaiby

  9. KACST‑TIC in Radio Frequency and Photonics for the E‑Society (RFTONICS), King Saud University, Riyadh, 11421, Saudi Arabia

    Saleh A. Alshebeili

  10. Department of Electrical Engineering, King Saud University, Riyadh, 11421, Saudi Arabia

    Saleh A. Alshebeili

  11. Department of Information Technology, College of Computer and Information sciences, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

    Fathi E. Abd El-Samie

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  1. Ali Ahmed Khalil
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Correspondence to Walid El-Shafai.

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Khalil, A.A., El Sayeid, M.I., Ibrahim, F.E. et al. Efficient frameworks for statistical seizure detection and prediction. J Supercomput 79, 17824–17858 (2023). https://doi.org/10.1007/s11227-022-05030-0

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