ReviewRoyal Society open science2023
Seizure detection with reduced electroencephalogram channels: research trends and outlook.
Review in Royal Society open science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
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Who cites it
10 citing papers in PubMed, 18 citations in OpenAlex.
- Assessing a reduced-channel algorithm for end-to-end seizure detection on multiday EEG using inter-rater agreement with epileptologists.Scientific reports · 2026Article
- Point-of-Care EEG for Non-Convulsive Seizure and Status Epilepticus: Advances, Limitations, and Future Directions.Journal of clinical medicine · 2026Review
- Near-Real-Time Epileptic Seizure Detection with Reduced EEG Electrodes: A BiLSTM-Wavelet Approach on the EPILEPSIAE Dataset.Brain sciences · 2026Article
- Automated detection of interictal epileptiform discharges with few electroencephalographic channels.Epilepsia · 2025Article
- Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare.Health science reports · 2025Article
- Single-channel seizure detection with clinical confirmation of seizure locations using CHB-MIT dataset.Frontiers in neurology · 2024Article
- Gershgorin circle theorem-based feature extraction for biomedical signal analysis.Frontiers in neuroinformatics · 2024Article
- Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.Biomedicines · 2023Article
- Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning.Sensors (Basel, Switzerland) · 2023Article
- Optimizing Electrode Configurations for Wearable EEG Seizure Detection Using Machine Learning.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epilepsy is a prevalent condition characterized by recurrent, unpredictable seizures. Monitoring with surface electroencephalography (EEG) is the gold standard for diagnosing epilepsy, but a time-consuming, uncomfortable and sometimes ineffective process for patients. Further, using EEG over a brief monitoring period has variable success, dependent on patient tolerance and seizure frequency. The availability of hospital resources and hardware and software specifications inherently restrict the options for comfortable, long-term data collection, resulting in limited data for training machine-learning models. This mini-review examines the current patient journey, providing an overview of the current state of EEG monitoring with reduced electrodes and automated channel reduction methods. Opportunities for improving data reliability through multi-modal data fusion are suggested. We assert the need for further research in electrode reduction to advance brain monitoring solutions towards portable, reliable devices that simultaneously offer patient comfort, perform ultra-long-term monitoring and expedite the diagnosis process.
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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.