Research Area: Machine Learning
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.
Keywords:
Epileptic Seizures Prediction
Electroencephalograms
empirical mode decomposition
scalp EEG
Machine Learning
Deep Learning
Author(s) Name: Syed Muhammad Usman,Muhammad Usman and Simon Fong
Journal name: Computational and Mathematical Methods in Medicine
Conferrence name:
Publisher name: Hindawi
DOI: 10.1155/2017/9074759
Volume Information:
