Methods and materials: The proposal is to pose the above task as a supervised classification problem and derive a mapping function that classifies each signal segment. Given the complexity of the signal patterns, it is difficult to a priori choose any particular classifier. Therefore, Genetic Programming (GP), a population based meta-heuristic for automatic program induction, is used to automatically search for the mapping functions. Two GP-based classifiers are used and extensively evaluated. The signals from epileptic seizures are obtained using the Kindling model of elicited epilepsy in rodent test subjects, for which a seizure was elicited and recorded on four separate days.
Results: Results show that signal segments from a single seizure can be used to derive accurate classifiers that generalize when tested on different signals from the same subject; i.e., GP can automatically produce accurate mapping functions for intra-subject classification. A large number of experiments are performed with the GP classifiers achieving good performance based on standard performance metrics. Moreover, a proof-of-concept real-world prototype is presented, where a GP classifier is transferred and hard-coded on an embedded system using a digital-to-analogue converter and a field programmable gate array, achieving a low average classification error of 14.55%, sensitivity values between 0.65 and 0.97, and specificity values between 0.86 and 0.94.
Conclusions: The proposed approach achieves good results for stage identification, particularly when compared with previous works that focus on this task. The results show that the problem of intra-class classification can be solved with a low error, and high sensitivity and specificity. Moreover, the limitations of the approach are identified and good operating configurations can be proposed based on the results.