We introduce a novel approach to signal classification based on evolving temporal pattern detectors (TPDs) that can find the occurrences of embedded temporal structures in discrete time signals and illustrate its application to characterizing the alcoholic brain using visually evoked response potentials. In contrast to conventional techniques used for most signal classification tasks, this approach unifies the feature extraction and classification steps. It makes no prior assumptions regarding the spectral characteristics of the data; it merely assumes that some temporal patterns exist that distinguish two classes of signals and therefore could be applied to new signal classification tasks where a body of prior work identifying important features does not exist. Evolutionary computation (EC) discovers a classifier by simply learning from the time series samples. The alcoholic classification (AC) problem consists of 2 sub-tasks, one spatial and one temporal: choosing a subset of electroencephalogram leads used to create a composite signal (the spatial task), and detecting temporal patterns in this signal that are more prevalent in the alcoholics than the controls (the temporal task). To accomplish this, a novel representation and crossover operator were devised that enable multiple feature subset tasks to be solved concurrently. Three TPD techniques are presented that differ in the mechanism by which partial credit is assigned to temporal patterns that deviate from the specified pattern. An EC approach is used for evolving a subset of sensors and the TPD specifications. We found evidence that partial credit does help evolutionary discovery. Regions on the skull of an alcoholic subject that produced abnormal electrical activity compared to the controls were located. These regions were consistent with prior findings in the literature. The classification accuracy was measured as the area under the receiver operator characteristic curve (ROC); the ROC area for the training set varied from 90.32% to 98.83% and for the testing set it varied from 87.17% to 95.9%. 
A novel approach to signal classification with an application to identifying the alcoholic brain