Automatic Epileptic Seizure Detection Using PSO-Based Feature Selection and Multilevel Spectral Analysis for EEG Signals

Algorithm 1

The proposed PSO-based feature selection algorithm for seizure detection

Parameters:

: population size;

: the number of patients;

: -fold cross-validation;

: max iteration of the PSO;

for in : do

 for in : do

  Data partitioning according to feature vectors of:

  , , and represent the training, validation, and test sets, respectively;

  Initialization population: Initialize position and velocity of each particle within permissible range;

  while t do

   for in : do

    Conduct 10-fold cross-validation on , and calculate average accuracy ;

    Evaluate the classification accuracy on ;

    Compute fitness according to (11);

    Update and optimum of ;

    Update the velocity and position of the particle ;

    Observe the , when the iteration achieves the best validation accuracy, the training will stop;

   Retrain and build the classifier on based on the selected feature subset;

   Measure test accuracy on the test set via the trained classifier;

  Select the feature subset with best test accuracy ;

Output: Optimal feature set;