ReviewJournal of personalized medicine2021
Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database-A Survey.
Review in Journal of personalized medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 92 citations in OpenAlex.
- Frequency-aware transformer networks for robust and generalizable EEG-based seizure detection.Scientific reports · 2026Article
- Unlocking Insights from Postictal EEGs: Investigating Predictive Markers of Seizure Recurrence.Annals of biomedical engineering · 2026Article
- MCLF: Montage consistent CNN-Liquid fusion for long-term scalp EEG seizure detection.MethodsX · 2026Article
- Computational modeling of seizure reconstruction and attenuation via model reference control.Scientific reports · 2026Article
- Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.Scientific reports · 2026Article
- Montage-guided fusion network for long-term scalp electroencephalography seizure detection.Frontiers in neuroinformatics · 2026Article
- An Innovative Method for Refractory Epilepsy Diagnosis Based on Microstate Analysis and Graph Convolutional Network.Journal of medical systems · 2025Article
- A comprehensive EEG dataset and performance assessment for Autism Spectrum Disorder.Scientific reports · 2025Article
- Graph convolution network-based eeg signal analysis: a review.Medical & biological engineering & computing · 2025Review
- CPRSCA-ResNet: a novel ResNet-based model with Channel-Partitioned Resolution Spatial-Channel Attention for EEG-based seizure detection.Frontiers in neuroscience · 2025Article
- Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives.Royal Society open science · 2024Article
- Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection.Physical and engineering sciences in medicine · 2024Article
- An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection.Scientific reports · 2024Article
- Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals.Sensors (Basel, Switzerland) · 2023Article
- Estimating Patient-Level Uncertainty in Seizure Detection Using Group-Specific Out-of-Distribution Detection Technique.Sensors (Basel, Switzerland) · 2023Article
- A New Fuzzy-Based Classification Method for Use in Smart/Precision Medicine.Bioengineering (Basel, Switzerland) · 2023Article
- Patient-specific approach using data fusion and adversarial training for epileptic seizure prediction.Frontiers in computational neuroscience · 2023Article
- Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer's Disease Subjects.Life (Basel, Switzerland) · 2022Article
- Weighted Brain Network Analysis on Different Stages of Clinical Cognitive Decline.Bioengineering (Basel, Switzerland) · 2022Article
- DWT-EMD Feature Level Fusion Based Approach over Multi and Single Channel EEG Signals for Seizure Detection.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epilepsy is a neurological disorder of the brain that causes frequent occurrence of seizures. Electroencephalography (EEG) is a tool that assists neurologists in detecting epileptic seizures caused by an unexpected flow of electrical activities in the brain. Automated detection of an epileptic seizure is a crucial task in diagnosing epilepsy which overcomes the drawback of a visual diagnosis. The dataset analyzed in this article, collected from Children's Hospital Boston (CHB) and the Massachusetts Institute of Technology (MIT), contains long-term EEG records from 24 pediatric patients. This review paper focuses on various patient-dependent and patient-independent personalized medicine approaches involved in the computer-aided diagnosis of epileptic seizures in pediatric subjects by analyzing EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers. This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify between seizure and non-seizure EEG signals. Performance metrics such as classification accuracy, sensitivity, and specificity were examined, and challenges in automatic seizure detection using the CHB-MIT database were addressed.
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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.