# An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals
## 4 Discussion  
We evaluated an ensemble feature selection approach to support the feature selection for the classification of EEG signals with epileptiform events. The evaluation considered three aspects: (i) evaluating the impact of the relevant features selected by the EFS method in the classification of segments of EEG signals, (ii) evaluating a classifier of normal or abnormal segments of EEG signals using a set of relevant features selected by the EFS method, and (iii) evaluating the stability of the EFS method for selecting features with different samples of the dataset. In the review of the state of the art, several studies were found that proposed approaches for building an ensemble method of feature selection algorithms [27; 28; 29]. However, most of the works were not applied to EEG datasets, and the results are not conclusive. Moreover, the works that proposed a kind of ensemble feature selection used an approach based on stages, where the first step selects the first subset of relevant features. Then, in the second stage, the subset chosen in the first stage is re-evaluated by another feature selection algorithm. Thus, the first stage could bias the second stage. Likewise, some authors propose solutions to build the ensemble of feature selection algorithms based on filters [30; 31; 32; 33]. However, although this kind of algorithm is simple and easy to implement, the algorithms based on filters have many weaknesses. In this sense, if the goal of an ensemble learning scheme is to combine the decision of different models to create robust choices, the idea to build an ensemble based on a filter could be a wrong decision. Besides, most of the studies of ensemble feature selection reviewed do not include stability as a metric to evaluate the quality of the feature selection process. Considering the results in Tables 1-4, the best results in the classification were achieved when the classifiers, i.e., decision tree, logistic regression, random forest, and SVM, used the subset of relevant features generated by the EFS method. Besides, the SVM was the algorithm that classified better for the evaluation performed to see the impact of the pertinent features of the learning process. Thus, the model built to classify normal and abnormal EEG signals was based on SVM and the relevant features selected by the EFS method. As a result, this classifier achieved an accuracy of 97.46%, a true negative rate of 96.43%, a true positive rate of 97.96%, a sensitivity of 96.78%, and a specificity of 97.95%. These values showed a performance equal to or greater than those found in studies reviewed in the literature. In the same way, a detector of epileptic activity was built to show the use of the classifier built in the context of the automatic reading of EEG signals and analyze the classifier's performance in a scenario where there is not a balanced dataset. An EEG record contains a large number of segments. However, most of them are normal segments. A reduced number of segments are abnormal, which generates an unbalanced scenario to evaluate the detection of abnormal EEGs as a binary classification task. Although the classifier used by the detector was trained using a perfect balanced dataset, the results showed an accuracy of 92.53%, sensitivity of 95.79%, and specificity of 92.48% in a scenario with an unbalanced dataset. Considering that early detection of epilepsy is critical to its treatment, the priority for the detector is to increase the probability that a segment detected as normal is a normal segment; this decreases the rate of false negatives and, consequently, reduces the likelihood of putting the patient's health at risk. Although the tests evidenced a low rate of false negatives, the detector has not been designed to replace the work of an expert, and its potential should be used to help the experts to identify abnormalities quickly and optimize their time. Besides, the detector allows validating the classifier's performance, which was trained using a balanced dataset.