ArticleSensors (Basel, Switzerland)2018
Integration of 24 Feature Types to Accurately Detect and Predict Seizures Using Scalp EEG Signals.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Machine Learning in Detecting Pediatric Epileptic Seizures: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Rapid delineation ofMicrobiology spectrum · 2026Article
- Electrophysiological biomarkers for foundational signal markers in chronic pancreatitis using machine learning models.Frontiers in pain research (Lausanne, Switzerland) · 2026Article
- Automatic detection and prediction of epileptic EEG signals based on nonlinear dynamics and deep learning: a review.Frontiers in neuroscience · 2025Review
- Smart Seizure Detection System: Machine Learning Based Model in Healthcare IoT.Current aging science · 2025Article
- Uncertainty-Aware Bayesian Deep Learning with Noisy Training Labels for Epileptic Seizure Detection.Uncertainty for safe utilization of machine learning in medical imaging : 6th international workshop, UNSURE 2024, held in conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, proceedings. UNSURE (Workshop) (6th : 2024 : ... · 2025Article
- Research progress of epileptic seizure prediction methods based on EEG.Cognitive neurodynamics · 2024Review
- A review of epilepsy detection and prediction methods based on EEG signal processing and deep learning.Frontiers in neuroscience · 2024Review
- DeepSOZ: A Robust Deep Model for Joint Temporal and Spatial Seizure Onset Localization from Multichannel EEG Data.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals-A Systematic Literature Review.Bioengineering (Basel, Switzerland) · 2022Review
- Hybrid machine learning method for a connectivity-based epilepsy diagnosis with resting-state EEG.Medical & biological engineering & computing · 2022Article
- The performance evaluation of the state-of-the-art EEG-based seizure prediction models.Frontiers in neurology · 2022Review
- Epilepsy Detection Based on Variational Mode Decomposition and Improved Sample Entropy.Computational intelligence and neuroscience · 2022Article
- Enhanced Feature Extraction-based CNN Approach for Epileptic Seizure Detection from EEG Signals.Journal of healthcare engineering · 2022Article
- A multi-frame network model for predicting seizure based on sEEG and iEEG data.Frontiers in computational neuroscience · 2022Article
- Convolutional Neural Network for Drowsiness Detection Using EEG Signals.Sensors (Basel, Switzerland) · 2021Article
- Fast Identification of Adverse Drug Reactions (ADRs) of Digestive and Nervous Systems of Organic Drugs by In Silico Models.Molecules (Basel, Switzerland) · 2021Article
- Expert-Level Intracranial Electroencephalogram Ictal Pattern Detection by a Deep Learning Neural Network.Frontiers in neurology · 2021Article
- EEG Channel-Selection Method for Epileptic-Seizure Classification Based on Multi-Objective Optimization.Frontiers in neuroscience · 2020Article
- Epileptic seizure detection using EEG signals and extreme gradient boosting.Journal of biomedical research · 2019Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The neurological disorder epilepsy causes substantial problems to the patients with uncontrolled seizures or even sudden deaths. Accurate detection and prediction of epileptic seizures will significantly improve the life quality of epileptic patients. Various feature extraction algorithms were proposed to describe the EEG signals in frequency or time domains. Both invasive intracranial and non-invasive scalp EEG signals have been screened for the epileptic seizure patterns. This study extracted a comprehensive list of 24 feature types from the scalp EEG signals and found 170 out of the 2794 features for an accurate classification of epileptic seizures. An accuracy (Acc) of 99.40% was optimized for detecting epileptic seizures from the scalp EEG signals. A balanced accuracy (bAcc) was calculated as the average of sensitivity and specificity and our seizure detection model achieved 99.61% in bAcc. The same experimental procedure was applied to predict epileptic seizures in advance, and the model achieved Acc = 99.17% for predicting epileptic seizures 10 s before happening.
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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.