EPILEPTIC SEIZURE PREDICTION USING WAVELET TRANSFORM, FRACTAL DIMENSION, SUPPORT VECTOR MACHINE, AND EEG SIGNALS

Andrea V. Perez-Sanchez, Martin Valtierra-Rodriguez, Carlos A. Perez-Ramirez, J. Jesus de-Santiago-Perez and Juan P. Amezquita-Sanchez
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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

FRACTALS (fractals), 2022, vol. 30, issue 07, 1-18

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%.

Keywords: Fractal Dimension Algorithms; Seizure Prediction; Electroencephalogram Signals; Discrete Wavelet Transform (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:wsi:fracta:v:30:y:2022:i:07:n:s0218348x22501547

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DOI: 10.1142/S0218348X22501547

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