Classification of epileptic EEG recordings using signal transforms and convolutional neural networks.
This paper describes the analysis of a deep neural network for the classification of epileptic EEG signals. The deep learning architecture is made up of two convolutional layers for feature extraction and three fully-connected layers for classification. We evaluated several EEG signal transforms for generating the inputs to the deep neural network: Fourier, wavelet and empirical mode decomposition. This analysis was carried out using two public datasets (Bern-Barcelona EEG and Epileptic Seizure Recognition datasets) obtaining significant improvements in accuracy. For the Bern-Barcelona EEG, we obtained an increase in accuracy from 92.3% to 98.9% when classifying between focal and non-focal signals using the empirical mode decomposition. For the Epileptic Seizure Recognition dataset, we evaluated several scenarios for seizure detection obtaining the best results when using the Fourier transform. The accuracy increased from 99.0% to 99.5% for classifying non-seizure vs. seizure recordings, from 91.7% to 96.5% when differentiating between healthy, non-focal and seizure recordings, and from 89.0% to 95.7% when considering healthy, focal and seizure recordings.
['San-Segundo|Rubén|R|', 'Gil-Martín|Manuel|M|', "D'Haro-Enríquez|Luis Fernando|LF|", 'Pardo|José Manuel|JM|']
D000077321:Deep Learning D004569:Electroencephalography D004827:Epilepsy D006801:Humans D012815:Signal Processing, Computer-Assisted
Convolutional neural networks Seizure detection Fourier transform Electroencephalogram Epilepsy Epileptic EEG signal classification Wavelet transform and Empirical Mode Decomposition (EMD)