ReviewBioengineering (Basel, Switzerland)2022
Supervised Machine Learning and Deep Learning Techniques for Epileptic Seizure Recognition Using EEG Signals-A Systematic Literature Review.
Review in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
19 citing papers in PubMed.
- Patient-independent hybrid generative-discriminative modeling for seizure detection in long-term scalp EEG.Scientific reports · 2026Article
- EEG-based harmful brain activity classification using deep learning and feature fusion.Scientific reports · 2026Article
- AI enabled decision support systems in epilepsy surgery a scoping review.Research square · 2026Article
- Explainable AI-Enhanced Ensemble Protocol Using Gradient-Boosted Models for Zero-False-Alarm Seizure Detection from EEG.Sensors (Basel, Switzerland) · 2026Article
- Deep learning enabled decision support systems in epilepsy surgery: a scoping review.npj health systems · 2026Article
- Epilepsy Prediction via Time-Frequency Features and Multi-Scale Hybrid Neural Networks.Journal of medical systems · 2025Article
- Advancements and Challenges of Artificial Intelligence-Assisted Electroencephalography in Epilepsy Management.Journal of clinical medicine · 2025Review
- Epileptic Seizure Detection Using Machine Learning: A Systematic Review and Meta-Analysis.Brain sciences · 2025Review
- Using vision transformers for electrographic seizure classification to aid physician review of intracranial electroencephalography recordings.Frontiers in human neuroscience · 2025Article
- Application of multi-feature-based machine learning models to predict neurological outcomes of cardiac arrest.Resuscitation plus · 2024Review
- Article
- Calibrating Deep Learning Classifiers for Patient-Independent Electroencephalogram Seizure Forecasting.Sensors (Basel, Switzerland) · 2024Article
- Artificial Intelligence and Its Revolutionary Role in Physical and Mental Rehabilitation: A Review of Recent Advancements.BioMed research international · 2024Review
- Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist.Current neurology and neuroscience reports · 2023Review
- Direct Estimation of Equivalent Bioelectric Sources Based on Huygens' Principle.Bioengineering (Basel, Switzerland) · 2023Article
- Physiological Noise Filtering in Functional Near-Infrared Spectroscopy Signals Using Wavelet Transform and Long-Short Term Memory Networks.Bioengineering (Basel, Switzerland) · 2023Article
- A New Hybrid Approach Based on Time Frequency Images and Deep Learning Methods for Diagnosis of Migraine Disease and Investigation of Stimulus Effect.Diagnostics (Basel, Switzerland) · 2023Article
- Prediction of Cognitive Load from Electroencephalography Signals Using Long Short-Term Memory Network.Bioengineering (Basel, Switzerland) · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Electroencephalography (EEG) is a complicated, non-stationary signal that requires extensive preprocessing and feature extraction approaches to be accurately analyzed. In recent times, Deep learning (DL) has shown great promise in exploiting the characteristics of EEG signals as it can learn relevant features from raw data autonomously. Although studies involving DL have become more common in the last two years, the topic of whether DL truly delivers advantages over conventional Machine learning (ML) methodologies remains unsettled. This study aims to present a detailed overview of the main challenges in the field of seizure detection, prediction, and classification utilizing EEG data, and the approaches taken to solve them using ML and DL methods. A systematic review was conducted surveying peer-reviewed publications published between 2017 and 16 July 2022 using two scientific databases (Web of Science and Scopus) totaling 6822 references after discarding duplicate publications. Whereas 2262 articles were screened based on the title, abstract, and keywords, only 214 were eligible for full-text assessment. A total of 91 papers have been included in this survey after meeting the eligible inclusion and exclusion criteria. The most significant findings from the review are summarized, and several important concepts involving ML and DL for seizure detection, prediction, and classification are discussed in further depth. This review aims to learn more about the different approaches for identifying different types and stages of epileptic seizures, which may then be employed to enhance the lives of epileptic patients in the future, as well as aid experts in the field.
Indexed as
Identifiers
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.