ReviewBrain informatics2020
A review of epileptic seizure detection using machine learning classifiers.
Review in Brain informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 82 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
82 citing papers in PubMed, 1 synthesis or guideline pooled it, 417 citations in OpenAlex.
- Artificial intelligence in electroencephalography analysis for epilepsy diagnosis and management.Frontiers in neurology · 2025Pooled it
- Capturing silent oxidative stress in early Alzheimer's disease: prediction of CSF biomarkers from sleep qEEG data.GeroScience · 2026Article
- Modern generative large language models in epilepsy care: a scoping review of current applications, challenges, and future directions.Acta epileptologica · 2026Review
- Clinical application of artificial intelligence technology in epilepsy.Acta epileptologica · 2026Review
- Effiformer: a unified data-efficient vision transformer-CNN framework for interpretable epileptic seizure detection.Brain informatics · 2026Article
- SinTransNet: an EEG-based deep learning framework for infantile epileptic spasms syndrome detection.BMC medical informatics and decision making · 2026Article
- Evaluating multi-level membership inference risk in federated EEG learning.Brain informatics · 2026Article
- Prediction of recurrent ischemic stroke using machine learning from real-world data.BMC medical research methodology · 2026Article
- AI-Driven Electrographic Seizure Classification and Seizure Onset Detection Using Image- and Time-Series-Based Approaches.IEEE transactions on bio-medical engineering · 2026Article
- Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.Scientific reports · 2026Article
- POCA: a CPG signal analysis algorithm using peak-based feature extraction and machine learning.Frontiers in neuroscience · 2026Article
- IESS-FusionNet: Physiologically Inspired EEG-EMG Fusion with Linear Recurrent Attention for Infantile Epileptic Spasms Syndrome Detection.Bioengineering (Basel, Switzerland) · 2025Article
- Mixture of checkpoint experts for explainable seizure detection using wearable devices.Scientific reports · 2025Article
- Prediction of Epilepsy Seizure Based on Cepstrum Analysis and Deep Learning.Interdisciplinary sciences, computational life sciences · 2025Article
- Neonatal seizure detection from EEG using inception ResNetV2 feature extraction and XGBoost optimized with particle swarm optimization.Scientific reports · 2025Article
- Seizure Type Classification Based on Hybrid Feature Engineering and Mutual Information Analysis Using Electroencephalogram.Entropy (Basel, Switzerland) · 2025Article
- Artificial intelligence in preclinical epilepsy research: Current state, potential, and challenges.Epilepsia open · 2025Review
- Language and Memory Network Alterations in Temporal Lobe Epilepsy: A Functional and Structural Connectivity Study.AJNR. American journal of neuroradiology · 2025Article
- Dynamic Graph Convolutional Network with Dilated Convolution for Epilepsy Seizure Detection.Bioengineering (Basel, Switzerland) · 2025Article
- SeizeIT2: Wearable Dataset Of Patients With Focal Epilepsy.Scientific data · 2025Article
22 more citing papers are in PubMed but not listed here.
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 at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epilepsy is a serious chronic neurological disorder, can be detected by analyzing the brain signals produced by brain neurons. Neurons are connected to each other in a complex way to communicate with human organs and generate signals. The monitoring of these brain signals is commonly done using Electroencephalogram (EEG) and Electrocorticography (ECoG) media. These signals are complex, noisy, non-linear, non-stationary and produce a high volume of data. Hence, the detection of seizures and discovery of the brain-related knowledge is a challenging task. Machine learning classifiers are able to classify EEG data and detect seizures along with revealing relevant sensible patterns without compromising performance. As such, various researchers have developed number of approaches to seizure detection using machine learning classifiers and statistical features. The main challenges are selecting appropriate classifiers and features. The aim of this paper is to present an overview of the wide varieties of these techniques over the last few years based on the taxonomy of statistical features and machine learning classifiers-'black-box' and 'non-black-box'. The presented state-of-the-art methods and ideas will give a detailed understanding about seizure detection and classification, and research directions in the future.
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What OpenQuestion holds
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.