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Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States
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
doi:10.1038/s41598-019-52152-2
pmid:31666621
pmcid:PMC6821856
fatcat:63mv5lizyzgb7jicwl5j6nvkz4