Title of article :
Epileptic Seizures Prediction Using Machine Learning Methods
Usman, Syed Muhammad Shaheed Zulfikar Ali Bhutto Institute of Science and Technology - Islamabad, Pakistan , Usman, Muhammad Shaheed Zulfikar Ali Bhutto Institute of Science and Technology - Islamabad, Pakistan , Fong, Simon University of Macau, Macau
Epileptic seizures occur due to disorder in brain functionality which can affect patient’s health. Prediction of epileptic seizures before
the beginning of the onset is quite useful for preventing the seizure by medication. Machine learning techniques and computational
methods are used for predicting epileptic seizures from Electroencephalograms (EEG) signals. However, preprocessing of EEG
signals for noise removal and features extraction are two major issues that have an adverse effect on both anticipation time and true
positive prediction rate. Therefore, we propose a model that provides reliable methods of both preprocessing and feature extraction.
Our model predicts epileptic seizures’ sufficient time before the onset of seizure starts and provides a better true positive rate. We
have applied empirical mode decomposition (EMD) for preprocessing and have extracted time and frequency domain features for
training a prediction model. The proposed model detects the start of the preictal state, which is the state that starts few minutes
before the onset of the seizure, with a higher true positive rate compared to traditional methods, 92.23%, and maximum anticipation
time of 33 minutes and average prediction time of 23.6 minutes on scalp EEG CHB-MIT dataset of 22 subjects.
EEG , EMD , CHB-MIT , Methods
Journal title :
Computational and Mathematical Methods in Medicine