ArticleScientific reports2019
Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Third-order entropy for spatiotemporal neural network characterization.Journal of neurophysiology · 2025Article
- The Eastern Association of Electroencephalographers: A Canadian/USA success story.Clinical neurophysiology practice · 2025Review
- A Review of EEG-based Localization of Epileptic Seizure Foci: Common Points with Multimodal Fusion of Brain Data.Journal of medical signals and sensors · 2024Review
- Epileptic seizure focus detection from interictal electroencephalogram: a survey.Cognitive neurodynamics · 2023Review
- Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity.Scientific reports · 2023Article
- Epileptic seizure prediction using successive variational mode decomposition and transformers deep learning network.Frontiers in neuroscience · 2022Article
- Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network.Biosensors · 2021Article
- Deep anomaly detection of seizures with paired stereoelectroencephalography and video recordings.Scientific reports · 2021Article
- A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction.Scientific reports · 2021Article
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Authors and funding
4 authors.
Funding
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Abstract
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 previous 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.
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