# An Ensemble Feature Selection Approach to Identify Relevant Features from EEG Signals
## 5 Conclusions  
In this study, we used an ensemble feature selection approach that integrates the advantages of several feature selection algorithms to improve the identification of the characteristics with high power of differentiation in the classification of normal and abnormal EEG signals.  
The discrimination was evaluated using several classifiers, i.e., decision tree, logistic regression, random forest, and SVM. This evaluation allowed demonstrating that machine learning models could improve their performance, discarding the features that are not relevant or represent noise.  
The classifier built using features selected by the EFS method achieved an accuracy (97.64%), sensitivity (96.78%), and specificity (97.95%) equal to or greater than the values found in the literature using only a subset of features selected instead of all features. Additionally, the perfect stability achieved in selecting features on different samples of the original dataset demonstrated the reliability of the feature selection process.  
Although the detector of epileptic segments decreased almost five percentage points in the accuracy (92.53%) and two percentage points in the sensitivity (95.97%) when it was tested with a highly unbalanced scenario, the achieved specificity (92.48%) meets the requirements of the medical context, where the specificity is the main priority because it is crucial avoid false negatives that put the patient's health at risk.  
The EFS used to select the subset of relevant features allowed the computational complexity of the classification of epileptic segments to be decreased, and it demonstrated that it is not necessary to calculate many features to describe epileptiform events and classify them well.  
Finally, the main contribution of this work was to validate the selection of relevant features by the ensemble feature selection method on a dataset of EEG signals. The evaluation results allowed us to confirm that the use of EFS could help us improve the reliability of classifiers and detectors of epileptiform events in EEG signals.  
[PERSON] conceptualized the idea, developed, and evaluated the proposal and wrote the original draft of the manuscript. [PERSON] and [PERSON] reviewed and edited the manuscript, proposed the methodology and supervised the research. All authors have read and agreed to the published version of the manuscript.  
The work was funded by a grant from the Colombian Agency of Science, Technology, and Innovation Colciencias under Call 647-2015, project \"Selection Mechanism of Relevant Features for Automatic Epileptic Seizures Detection\". The funder provided support in the form of a scholarship for [PERSON]. Still, the funder did not have any additional role in the study design, data collection, analysis, decision to publish, or manuscript preparation. The specific role of [PERSON] is articulated in the 'Author Contributions' section.  
The study was conducted according to the guidelines approved by the Ethics Committee of the University of Cauca.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
The dataset used to evaluate EFS and train the classification model is available at [[https://github.com/Maritzag/EEGSignals/tree/master/EvaluationEFS](https://github.com/Maritzag/EEGSignals/tree/master/EvaluationEFS)]([https://github.com/Maritzag/EEGSignals/tree/master/EvaluationEFS](https://github.com/Maritzag/EEGSignals/tree/master/EvaluationEFS)), accessed on 15 May 2021.  
The authors declare no conflict of interest. The funders had no role in the study's design; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.