# Classification of EEG Signals using Genetic Programming for Feature Construction

## TL;DR

This paper introduces a genetic programming framework for automated feature construction from EEG signals, improving the detection of sleep spindles and K-complexes with better accuracy and balanced metrics compared to traditional methods.

## Contribution

The paper presents a novel GP-based method for feature construction and dimensionality reduction in EEG analysis, enhancing automatic detection of sleep structures.

## Key findings

- GP features outperformed PCA in AUC scores
- Better balance of Specificity and Recall achieved
- Insights for improving EEG data acquisition protocols

## Abstract

The analysis of electroencephalogram (EEG) waves is of critical importance for the diagnosis of sleep disorders, such as sleep apnea and insomnia, besides that, seizures, epilepsy, head injuries, dizziness, headaches and brain tumors. In this context, one important task is the identification of visible structures in the EEG signal, such as sleep spindles and K-complexes. The identification of these structures is usually performed by visual inspection from human experts, a process that can be error prone and susceptible to biases. Therefore there is interest in developing technologies for the automated analysis of EEG. In this paper, we propose a new Genetic Programming (GP) framework for feature construction and dimensionality reduction from EEG signals. We use these features to automatically identify spindles and K-complexes on data from the DREAMS project. Using 5 different classifiers, the set of attributes produced by GP obtained better AUC scores than those obtained from PCA or the full set of attributes. Also, the results obtained from the proposed framework obtained a better balance of Specificity and Recall than other models recently proposed in the literature. Analysis of the features most used by GP also suggested improvements for data acquisition protocols in future EEG examinations.