A low complexity solution for epilepsy detection using an improved version of the reaction-diffusion transform

Recognition of epileptic seizures is an important issue and in certain circumstances it is desirable to have portable equipment implementing the algorithm in order to better monitor the patients. This work considers a widely used EEG database from University of Bonn as reference for comparing our recognition method with other previously reported. In order to perform epileptic seizures we combine a low complexity nonlinear transform applied to the EEG time-series with a fast support vector classifier (FSVC), a low complexity classifier introduced in previous works. Since EEG signals used in epilepsy detection are complex signals produced in a large network of neurons it make sense to extract proper features using an optimized version of a nonlinear transform (previously introduced as the reaction-diffusion transform — RDT) having its roots in our previous work in the field of cellular nonlinear networks. Results reported here confirm that very good performance (98.67% accuracy) can be obtained, while using a very low complexity solution which is easy to integrate in a portable device. Since RDT provided already very good results in recognizing speech commands, there is a good evidence to consider it as an useful preprocessing method for a wider range of signals or time-series to be recognized.