Rapid Seizure Classification Using Feature Extraction and Channel Selection | Biomedgrid

The Seizure is an abnormal electrical activity in the brain; it can be diagnosed by a neurologist and could be classified using recorded data. Medical data, such as EEG signal usually contain many features and attributes that are not important for the classification process. Dimension reduction is an important step to reduce irrelevant information. Features extraction is one algorithm for dimension reduction step. Another one is the channel selection algorithm. These algorithms speed up the process of classification and improve accuracy. This paper proposes an approach based on extracting EEG features, channel selection to reduce the computation capacity, and trained model used for classification. Variance parameter is used for channels selection, by taking the maximum three ones. Eleven features are extracted from the selected channels and averaged to be the input for the classifier. Six classifiers are used to select the most accurate one. Ensemble classifier was the more accurate one to classify all seizure cases correctly as it is got 100% sensitivity for continuous testing and 97.6% for the random testing set.