ReviewComputational and mathematical methods in medicine2022
Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches.
Review in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 19 papers, 2 of them syntheses that pooled it.
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, 2 syntheses or guidelines pooled it, 84 citations in OpenAlex.
- Brain Gray Matter Alterations in Hepatic Encephalopathy: A Voxel-Based Meta-Analysis of Whole-Brain Studies.Frontiers in human neuroscience · 2022Pooled it
- EEG-Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review.Computational intelligence and neuroscience · 2022Pooled it
- Predicting Epileptic Seizures From EEG Signals Using Machine Learning.Neurosciences (Riyadh, Saudi Arabia) · 2026Article
- An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.Scientific reports · 2025Article
- Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare.Health science reports · 2025Article
- Sustainable E-Health: Energy-Efficient Tiny AI for Epileptic Seizure Detection via EEG.Biomedical engineering and computational biology · 2025Article
- Using vision transformers for electrographic seizure classification to aid physician review of intracranial electroencephalography recordings.Frontiers in human neuroscience · 2025Article
- Supervised and Unsupervised Deep Learning Approaches for EEG Seizure Prediction.Journal of healthcare informatics research · 2024Article
- Role of machine learning in the management of epilepsy: a systematic review protocol.BMJ open · 2024Article
- A Novel Prognostic Model Using Chaotic CNN with Hybridized Spoofing for Enhancing Diagnostic Accuracy in Epileptic Seizure Prediction.Diagnostics (Basel, Switzerland) · 2023Article
- Patient-Specific Preictal Pattern-Aware Epileptic Seizure Prediction with Federated Learning.Sensors (Basel, Switzerland) · 2023Article
- Review
- An effective fusion model for seizure prediction: GAMRNN.Frontiers in neuroscience · 2023Article
- Electroencephalography and mild cognitive impairment research: A scoping review and bibliometric analysis (ScoRBA).AIMS neuroscience · 2023Article
- Retracted: Review on Epileptic Seizure Prediction: Machine Learning and Deep Learning Approaches.Computational and mathematical methods in medicine · 2023Article
- Detection of epileptic seizures through EEG signals using entropy features and ensemble learning.Frontiers in human neuroscience · 2022Article
- The performance evaluation of the state-of-the-art EEG-based seizure prediction models.Frontiers in neurology · 2022Review
- A multimodal clinical data resource for personalized risk assessment of sudden unexpected death in epilepsy.Frontiers in big data · 2022Article
- A Review on Machine Learning Approaches in Identification of Pediatric Epilepsy.SN computer science · 2022Article
Corrections and comments
- Retraction · 2023-12-13Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epileptic seizures occur due to brain abnormalities that can indirectly affect patient's health. It occurs abruptly without any symptoms and thus increases the mortality rate of humans. Almost 1% of world's population suffers from epileptic seizures. Prediction of seizures before the beginning of onset is beneficial for preventing seizures by medication. Nowadays, modern computational tools, machine learning, and deep learning methods have been used to predict seizures using EEG. However, EEG signals may get corrupted with background noise, and artifacts such as eye blinks and physical movements of muscles may lead to "pops" in the signal, resulting in electrical interference, which is cumbersome to detect through visual inspection for longer duration recordings. These limitations in automatic detection of interictal spikes and epileptic seizures are preferred, which is an essential tool for examining and scrutinizing the EEG recording more precisely. These restrictions bring our attention to present a review of automated schemes that will help neurologists categorize epileptic and nonepileptic signals. While preparing this review paper, it is observed that feature selection and classification are the main challenges in epilepsy prediction algorithms. This paper presents various techniques depending on various features and classifiers over the last few years. The methods presented will give a detailed understanding and ideas about seizure prediction and future research directions.
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.