ArticleScientific reports2018
Bispectrum Features and Multilayer Perceptron Classifier to Enhance Seizure Prediction.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Third-order entropy for spatiotemporal neural network characterization.Journal of neurophysiology · 2025Article
- Application of Data Mining Technology in the Screening for Gallbladder Stones: A Cross-Sectional Retrospective Study of Chinese Adults.Yonsei medical journal · 2024Article
- Third-order motifs are sufficient to fully and uniquely characterize spatiotemporal neural network activity.Scientific reports · 2023Article
- Machine Learning and Deep Learning CT-Based Models for Predicting the Primary Central Nervous System Lymphoma and Glioma Types: A Multicenter Retrospective Study.Frontiers in neurology · 2022Article
- Augmented detection of septal defects using advanced optical coherence tomography network-processed phonocardiogram.Frontiers in cardiovascular medicine · 2022Article
- Power efficient refined seizure prediction algorithm based on an enhanced benchmarking.Scientific reports · 2021Article
- Fusion of Higher Order Spectra and Texture Extraction Methods for Automated Stroke Severity Classification with MRI Images.International journal of environmental research and public health · 2021Article
- Seizure Susceptibility Prediction in Uncontrolled Epilepsy.Frontiers in neurology · 2021Article
- Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting.Entropy (Basel, Switzerland) · 2020Article
- Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States.Scientific reports · 2019Article
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5 authors.
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Abstract
The ability to accurately forecast seizures could significantly improve the quality of life of patients with drug-refractory epilepsy. Prediction capabilities rely on the adequate identification of seizure activity precursors from electroencephalography recordings. Although a long list of features has been proposed, none of these is able to independently characterize the brain states during transition to a seizure. This work assessed the feasibility of using the bispectrum, an advanced signal processing technique based on higher order statistics, as a precursor of seizure activity. Quantitative features were extracted from the bispectrum and passed through two statistical tests to check for significant differences between preictal and interictal recordings. Results showed statistically significant differences (p < 0.05) between preictal and interictal states using all bispectrum-extracted features. We used normalized bispectral entropy, normalized bispectral squared entropy, and mean of magnitude as inputs to a 5-layer multilayer perceptron classifier and achieved respective held-out test accuracies of 78.11%, 72.64%, and 73.26%.
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