Using Support Vector Machines as an Intelligent Algorithm for Detecting Seizures from EEG Signals

1. Seizures 2. Electroencephalography 3. Passive Cutaneous Anaphylaxis Introduction: Electroencephalography (EEG) is the mos t commonly used method to s tudy the function of the brain. This s tudy represents a computerized model for dis tinguishing between epileptic and healthy subjects using EEG signals with relatively high accuracy. Materials and Methods: The EEG database used in this s tudy was obtained from the data available in Andrzejak. This dataset consis ts of 5 EEG sets (designated as A to E), each containing 100 EEG sections. Collections A and B comprised EEG signals that have been taken from 5 healthy volunteers. The C and D sets referred to EEGs from patients with focal epilepsy (without ictal recordings) and the E set was derived from a patient with ictal recording. Support vector machines were used after applying principal components analysis or linear discriminant analysis over the features of the signals. MATLAB has been used to implement and tes t the proposed classification algorithm. To evaluate the proposed method, the confusion matrix, overall success rate, ROC, and the AUC of each class were extracted. K-fold cross-validation technique was used to validate the results. Results: The overall success rate achieved in this s tudy was above 82%. Dimension reduction algorithms can improve its accuracy and speed. Conclusion: It is helpful to be able to predict the occurrence of a seizure early and accurately. Using the computerized model represented in this s tudy could accomplish this goal.l ABSTRACT Article Info: