IDEAS home Printed from https://ideas.repec.org/a/wsi/fracta/v30y2022i07ns0218348x22501547.html
   My bibliography  Save this article

Epileptic Seizure Prediction Using Wavelet Transform, Fractal Dimension, Support Vector Machine, And Eeg Signals

Author

Listed:
  • ANDREA V. PEREZ-SANCHEZ

    (ENAP-Research Group, CA-Sistemas Dinámicos y Control, Facultad de Ingeniería Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río, Querétaro, C. P. 76807, México)

  • MARTIN VALTIERRA-RODRIGUEZ

    (ENAP-Research Group, CA-Sistemas Dinámicos y Control, Facultad de Ingeniería Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río, Querétaro, C. P. 76807, México)

  • CARLOS A. PEREZ-RAMIREZ

    (��ENAP-Research Group, Facultad de Ingeniería, Universidad Autónoma de Querétaro (UAQ), Campus Aeropuerto, Carretera a Chichimequillas S/N, Ejido Bolaños 76140, Santiago de Querétaro, Querétaro, México)

  • J. JESUS DE-SANTIAGO-PEREZ

    (ENAP-Research Group, CA-Sistemas Dinámicos y Control, Facultad de Ingeniería Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río, Querétaro, C. P. 76807, México)

  • JUAN P. AMEZQUITA-SANCHEZ

    (ENAP-Research Group, CA-Sistemas Dinámicos y Control, Facultad de Ingeniería Universidad Autónoma de Querétaro (UAQ), Campus San Juan del Río, Río Moctezuma 249, Col. San Cayetano, San Juan del Río, Querétaro, C. P. 76807, México)

Abstract

Epilepsy, a neurological disorder, affects millions of persons worldwide. It is distinguished by causing recurrent seizures in patients, which can conduct to severe health problems. Consequently, it is essential to offer a method capable of timely predicting a seizure before its appearance, so patients can avoid possible injuries by taking preventive action. In this sense, a method based on the integration of discrete wavelet transform (DWT), fractal dimension, and support vector machine (SVM) is presented for the prediction of an epileptic seizure up to 30min before its onset through the analysis of electroencephalogram (EEG) signals. DWT is initially applied to the EEG signals to obtain their main neurological bands; then, five fractal dimension indices (e.g. Sevcik, Petrosian, Box, Higuchi, and Katz) are explored as potential seizure indicators. Finally, an SVM is developed to predict the epileptic seizure automatically. The effectiveness of the proposal to predict an epileptic crisis is validated by employing a database of 14 subjects with 42 epileptic seizures provided by the Massachusetts Institute of Technology and the Children’s Hospital Boston. The results demonstrate that the proposal can predict an epileptic seizure up to 30min before its onset with a high accuracy of 93.33%.

Suggested Citation

  • Andrea V. Perez-Sanchez & Martin Valtierra-Rodriguez & Carlos A. Perez-Ramirez & J. Jesus De-Santiago-Perez & Juan P. Amezquita-Sanchez, 2022. "Epileptic Seizure Prediction Using Wavelet Transform, Fractal Dimension, Support Vector Machine, And Eeg Signals," FRACTALS (fractals), World Scientific Publishing Co. Pte. Ltd., vol. 30(07), pages 1-18, November.
    • Handle: RePEc:wsi:fracta:v:30:y:2022:i:07:n:s0218348x22501547
    DOI: 10.1142/S0218348X22501547
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0218348X22501547
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0218348X22501547?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:fracta:v:30:y:2022:i:07:n:s0218348x22501547. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: https://www.worldscientific.com/worldscinet/fractals .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.