Classification of Two-channel Signals by Means of Genetic Programming

TL;DR

This paper introduces a novel genetic programming-based method for automatic signal classification that eliminates the need for prior expert knowledge, demonstrated on EEG data related to epilepsy with high accuracy.

Contribution

It presents an automatic feature extraction approach using genetic programming for signal classification, removing the reliance on human expert feature selection.

Findings

Achieved high classification accuracy on EEG epilepsy data

Demonstrated effectiveness of automatic feature extraction

Reduced need for prior signal knowledge

Abstract

Traditionally, signal classification is a process in which previous knowledge of the signals is needed. Human experts decide which features are extracted from the signals, and used as inputs to the classification system. This requirement can make significant unknown information of the signal be missed by the experts and not be included in the features. This paper proposes a new method that automatically analyses the signals and extracts the features without any human participation. Therefore, there is no need for previous knowledge about the signals to be classified. The proposed method is based on Genetic Programming and, in order to test this method, it has been applied to a well-known EEG database related to epilepsy, a disease suffered by millions of people. As the results section shows, high accuracies in classification are obtained