ReviewDiagnostics (Basel, Switzerland)2023
Epileptic Seizure Detection Using Machine Learning: Taxonomy, Opportunities, and Challenges.
Review in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 82 citations in OpenAlex.
- Power and Phase Fusion Spectrogram with Three-Dimensional Convolution and Vision Transformer for Seizure Detection.Diagnostics (Basel, Switzerland) · 2026Article
- SinTransNet: an EEG-based deep learning framework for infantile epileptic spasms syndrome detection.BMC medical informatics and decision making · 2026Article
- Patient-independent hybrid generative-discriminative modeling for seizure detection in long-term scalp EEG.Scientific reports · 2026Article
- From Annotation to Prediction: Hospital-Grade Early Seizure Risk Prediction from Adult EEG.Diagnostics (Basel, Switzerland) · 2026Article
- Explainable AI-Enhanced Ensemble Protocol Using Gradient-Boosted Models for Zero-False-Alarm Seizure Detection from EEG.Sensors (Basel, Switzerland) · 2026Article
- Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.Scientific reports · 2026Article
- Computational Approaches to Neurological Disorder Diagnosis: An In-Depth Review of Current Methods and Future ProspectsCurrent medical imaging · 2026Review
- Mixture of checkpoint experts for explainable seizure detection using wearable devices.Scientific reports · 2025Article
- Epileptic seizure detection from electroencephalogram signals based on 1D CNN-LSTM deep learning model using discrete wavelet transform.Scientific reports · 2025Article
- Sustainable E-Health: Energy-Efficient Tiny AI for Epileptic Seizure Detection via EEG.Biomedical engineering and computational biology · 2025Article
- Diagnosis of epileptic seizure neurological condition using EEG signal: a multi-model algorithm.Frontiers in medicine · 2025Article
- Deep learning approaches for diagnosing seizure based on EEG signal analysis.Frontiers in human neuroscience · 2025Article
- Improved attention-based PCNN with GhostNet for epilepsy seizure detection using EEG and fMRI modalities: extractive pattern and histogram feature set.Frontiers in artificial intelligence · 2025Article
- Epileptic seizure prediction via multidimensional transformer and recurrent neural network fusion.Journal of translational medicine · 2024Article
- Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection.Physical and engineering sciences in medicine · 2024Article
- Using Explainable Artificial Intelligence to Obtain Efficient Seizure-Detection Models Based on Electroencephalography Signals.Sensors (Basel, Switzerland) · 2023Article
- Identification of TLE Focus from EEG Signals by Using Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Automatic Detection of Abnormal EEG Signals Using WaveNet and LSTM.Sensors (Basel, Switzerland) · 2023Article
- Method for Automatic Estimation of Instantaneous Frequency and Group Delay in Time-Frequency Distributions with Application in EEG Seizure Signals Analysis.Sensors (Basel, Switzerland) · 2023Article
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Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many years, studies have been conducted to support the automatic diagnosis of epileptic seizures for clinicians' ease. For that, several studies entail machine learning methods for early predicting epileptic seizures. Mainly, feature extraction methods have been used to extract the right features from the EEG data generated by the EEG machine. Then various machine learning classifiers are used for the classification process. This study provides a systematic literature review of the feature selection process and classification performance. This review was limited to finding the most used feature extraction methods and the classifiers used for accurate classification of normal to epileptic seizures. The existing literature was examined from well-known repositories such as MDPI, IEEE Xplore, Wiley, Elsevier, ACM, Springer link, and others. Furthermore, a taxonomy was created that recapitulates the state-of-the-art used solutions for this problem. We also studied the nature of different benchmark and unbiased datasets and gave a rigorous analysis of the working of classifiers. Finally, we concluded the research by presenting the gaps, challenges, and opportunities that can further help researchers predict epileptic seizures.
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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.