Combined neural network model employing wavelet coefficients for EEG signals classification

This paper illustrates the use of combined neural network model to guide model selection for classification of electroencephalogram (EEG) signals. The EEG signals were decomposed into time–frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the EEG signals classification using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified with the accuracy of 94.83% by the combined neural network. The combined neural network model achieved accuracy rates which were higher than that of the stand-alone neural network model.