Classification of Two-channel Signals by Means of Genetic Programming
This addresses the issue of potentially missing unknown signal information in classification for applications like epilepsy diagnosis, though it is incremental as it applies an existing technique to a new domain.
The paper tackles the problem of signal classification requiring human expert knowledge for feature extraction by proposing a method based on Genetic Programming that automatically analyzes and extracts features without human participation. It achieves high accuracies in classification when applied to an EEG database related to epilepsy.
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