Abstract：
:Combining multiple linear univariate features in one feature space and classifying the feature space using machine learning methods could predict epileptic seizures in patients suffering from refractory epilepsy. For each patient, a set of twenty-two linear univariate features were extracted from 6 electroencephalogram (EEG) signals to make a 132 dimensional feature space. Preprocessing and normalization methods of the features, which affect the output of the seizure prediction algorithm, were studied in terms of alarm sensitivity and false prediction rate (FPR). The problem of choosing an optimal preictal time was tackled using 4 distinct values of 10, 20, 30, and 40 min. The seizure prediction problem has traditionally been considered a two-class classification problem, which is also exercised here. These studies have been conducted on the features obtained from 10 patients. For each patient, 48 different combinations of methods are compared to find the best configuration. Normalization by dividing by the maximum and smoothing are found to be the best configuration in most of the patients. The results also indicate that applying machine learning methods on a multidimensional feature space of 22 univariate features predicted seizure onsets with high performance. On average, the seizures were predicted in 73.9% of the cases (34 out of 46 in 737.9h of test data), with a FPR of 0.15 h(-1).
journal_name
J Neurosci Methodsjournal_title
Journal of neuroscience methodsauthors
Rasekhi J,Mollaei MR,Bandarabadi M,Teixeira CA,Dourado Adoi
10.1016/j.jneumeth.2013.03.019subject
Has Abstractpub_date
2013-07-15 00:00:00pages
9-16issue
1-2eissn
0165-0270issn
1872-678Xpii
S0165-0270(13)00124-6journal_volume
217pub_type
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