Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States

Laura Gagliano, Elie Bou Assi, Dang K. Nguyen, Mohamad Sawan
2019 Scientific Reports  
This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to
more » ... work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction.
doi:10.1038/s41598-019-52152-2 pmid:31666621 pmcid:PMC6821856 fatcat:63mv5lizyzgb7jicwl5j6nvkz4