ReviewFrontiers in systems neuroscience2021
Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals.
Review in Frontiers in systems neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- CMEpiNet: Complex-Valued Multimodal Epilepsy Detection Network Model.Sensors (Basel, Switzerland) · 2026Article
- Identify MRI negative temporal lobe epilepsy with resting fMRI indicators and machine learning techniques.Scientific reports · 2025Article
- A novel epileptic seizure prediction model based on Cox-Stuart and Optuna.Frontiers in neurology · 2025Article
- Artificial intelligence in epilepsy - applications and pathways to the clinic.Nature reviews. Neurology · 2024Review
- A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning.Frontiers in neuroscience · 2024Review
- A novel method for optimizing epilepsy detection features through multi-domain feature fusion and selection.Frontiers in computational neuroscience · 2024Article
- A Novel Prognostic Model Using Chaotic CNN with Hybridized Spoofing for Enhancing Diagnostic Accuracy in Epileptic Seizure Prediction.Diagnostics (Basel, Switzerland) · 2023Article
- Systemic inflammation as a biomarker of seizure propensity and a target for treatment to reduce seizure propensity.Epilepsia open · 2023Review
- The performance evaluation of the state-of-the-art EEG-based seizure prediction models.Frontiers in neurology · 2022Review
- The decreased connectivity in middle temporal gyrus can be used as a potential neuroimaging biomarker for left temporal lobe epilepsy.Frontiers in psychiatry · 2022Article
- Wavelet Ridges in EEG Diagnostic Features Extraction: Epilepsy Long-Time Monitoring and Rehabilitation after Traumatic Brain Injury.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epilepsy is one of the most common neurological disorders typically characterized by recurrent and uncontrollable seizures, which seriously affects the quality of life of epilepsy patients. The effective tool utilized in the clinical diagnosis of epilepsy is the Electroencephalogram (EEG). The emergence of machine learning promotes the development of automated epilepsy detection techniques. New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. And then, the future development trend of epilepsy detection technology has prospected at the end of the article.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.