ArticleInternational journal of environmental research and public health2022
Epilepsy Seizures Prediction Based on Nonlinear Features of EEG Signal and Gradient Boosting Decision Tree.
Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Preoperative MEG reveals differential brain network characteristics in drug-resistant epilepsy patients based on vagus nerve stimulation response.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Research on epilepsy detection methods based on interpretable features and machine learning.PloS one · 2026Article
- From fragmented neurotechnologies to closed-loop brain health systems: integrating artificial intelligence, digital health, digital twins, and advanced materials for brain disorders.Frontiers in bioengineering and biotechnology · 2026Article
- Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model.Diagnostics (Basel, Switzerland) · 2025Article
- Epileptic seizure prediction via multidimensional transformer and recurrent neural network fusion.Journal of translational medicine · 2024Article
- A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction.Scientific reports · 2024Article
- Parallel Ictal-Net, a Parallel CNN Architecture with Efficient Channel Attention for Seizure Detection.Sensors (Basel, Switzerland) · 2024Article
- Multi-branch fusion graph neural network based on multi-head attention for childhood seizure detection.Frontiers in physiology · 2024Article
- Multi-perspective characterization of seizure prediction based on microstate analysis.Frontiers in neuroscience · 2024Article
- Effective Early Detection of Epileptic Seizures through EEG Signals Using Classification Algorithms Based on t-Distributed Stochastic Neighbor Embedding and K-Means.Diagnostics (Basel, Switzerland) · 2023Article
- An end-to-end seizure prediction approach using long short-term memory network.Frontiers in human neuroscience · 2023Article
- A Novel Epilepsy Detection Method Based on Feature Extraction by Deep Autoencoder on EEG Signal.International journal of environmental research and public health · 2022Article
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3 authors.
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Abstract
Epilepsy is a common neurological disorder with sudden and recurrent seizures. Early prediction of seizures and effective intervention can significantly reduce the harm suffered by patients. In this paper, a method based on nonlinear features of EEG signal and gradient boosting decision tree (GBDT) is proposed for early prediction of epilepsy seizures. First, the EEG signals were divided into two categories: those that had seizures onset over a period of time (represented by InT) and those that did not. Second, the noise in the EEG was removed using complementary ensemble empirical mode decomposition (CEEMD) and wavelet threshold denoising. Third, the nonlinear features of the two categories of EEG were extracted, including approximate entropy, sample entropy, permutation entropy, spectral entropy and wavelet entropy. Fourth, a GBDT classifier with random forest as the initial result was designed to distinguish the two categories of EEG. Fifth, a two-step "k of n" method was used to reduce the number of false alarms. The proposed method was evaluated on 13 patients' EEG data from the CHB-MIT Scalp EEG Database. Based on ten-fold cross validation, the average accuracy was 91.76% when the InT was taken at 30 min, and 38 out of 39 seizures were successfully predicted. When the InT was taken for 40 min, the average accuracy was 92.50% and all 42 seizures selected were successfully predicted. The results indicate the effectiveness of the proposed method for predicting epilepsy seizures.
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