ArticleJournal of biomedical research2020
Epileptic seizure prediction based on EEG spikes detection of ictal-preictal states.
Article in Journal of biomedical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Focused ultrasound for treatment of epilepsy: a systematic review and meta-analysis of preclinical and clinical studies.Neurosurgical review · 2024Pooled it
- Enhancing seizure prediction using a DC-SA-EBiLSTM framework with self-attention mechanism.Frontiers in neuroscience · 2026Article
- Research on epilepsy detection methods based on interpretable features and machine learning.PloS one · 2026Article
- Real-Time Multi-Channel Epileptic Seizure Detection Exploiting an Ultra-Low-Complexity Algorithm-Hardware Co-Design Approach.Sensors (Basel, Switzerland) · 2025Article
- Research progress of epileptic seizure prediction methods based on EEG.Cognitive neurodynamics · 2024Review
- Motor Behavior Regulation of Rat Robots Using Integrated Electrodes Stimulated by Micro-Nervous System.Micromachines · 2024Article
- Article
- Epileptic Seizure Prediction Based on Hybrid Seek Optimization Tuned Ensemble Classifier Using EEG Signals.Sensors (Basel, Switzerland) · 2022Article
- Moving the field forward: detection of epileptiform abnormalities on scalp electroencephalography using deep learning-clinical application perspectives.Brain communications · 2022Review
- Editorial commentary on special issue of Advances in EEG Signal Processing and Machine Learning for Epileptic Seizure Detection and Prediction.Journal of biomedical research · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epileptic seizures are known for their unpredictable nature. However, recent research provides that the transition to seizure event is not random but the result of evidence accumulations. Therefore, a reliable method capable to detect these indications can predict seizures and improve the life quality of epileptic patients. Seizures periods are generally characterized by epileptiform discharges with different changes including spike rate variation according to the shapes, spikes, and the amplitude. In this study, spike rate is used as the indicator to anticipate seizures in electroencephalogram (EEG) signal. Spikes detection step is used in EEG signal during interictal, preictal, and ictal periods followed by a mean filter to smooth the spike number. The maximum spike rate in interictal periods is used as an indicator to predict seizures. When the spike number in the preictal period exceeds the threshold, an alarm is triggered. Using the CHB-MIT database, the proposed approach has ensured 92% accuracy in seizure prediction for all patients.
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