ReviewBrain sciences2021
A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal.
Review in Brain sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- TFFBN-HDLF: a hybrid deep learning framework based on time-frequency functional brain networks for epileptic seizure detection.Frontiers in medicine · 2026Article
- ICU-EEG Pattern Detection by a Convolutional Neural Network.Annals of clinical and translational neurology · 2025Article
- EEG-Based Engagement Monitoring in Cognitive Games.Sensors (Basel, Switzerland) · 2025Article
- EPIC-NET: EEG-based epilepsy classification and brain localization using Optuna wave-gated recurrent unit network.Frontiers in computational neuroscience · 2025Article
- A Real-Time Epilepsy Detection Method Using Embedded Zero Tree Wavelet Approach and Support Vector Machine.Behavioural neurology · 2025Article
- Effectual seizure detection using MBBF-GPSO with CNN network.Cognitive neurodynamics · 2024Article
- An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detection.Scientific reports · 2024Article
- Machine learning and deep learning approach for medical image analysis: diagnosis to detection.Multimedia tools and applications · 2022Article
- A Novel Epilepsy Detection Method Based on Feature Extraction by Deep Autoencoder on EEG Signal.International journal of environmental research and public health · 2022Article
- SASDL and RBATQ: Sparse Autoencoder With Swarm Based Deep Learning and Reinforcement Based Q-Learning for EEG Classification.IEEE open journal of engineering in medicine and biology · 2022Article
- Wavelet Ridges in EEG Diagnostic Features Extraction: Epilepsy Long-Time Monitoring and Rehabilitation after Traumatic Brain Injury.Sensors (Basel, Switzerland) · 2021Article
- Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification. Hybrid deep learning has also been explored, with CNN-RNN being the most popular.
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