ArticleDiagnostics (Basel, Switzerland)2022
Application of Machine Learning in Epileptic Seizure Detection.
Article in Diagnostics (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, 1 of them a synthesis that pooled it.
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Who cites it
19 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
- Artificial Intelligence and Digital Biomarkers for Early Detection and Monitoring of Neurological Disorders: A Narrative Review.Diagnostics (Basel, Switzerland) · 2026Review
- Hybrid quantum classical framework for electroencephalogram driven neurological processing in epileptic seizure taxonomy.Scientific reports · 2026Article
- Article
- Epileptic spasm recognition: EEG classification using time-frequency features and machine learning.Biomedical engineering online · 2025Article
- Mixture of checkpoint experts for explainable seizure detection using wearable devices.Scientific reports · 2025Article
- Epileptic Seizure Detection Using Hyperdimensional Computing and Binary Naive Bayes Classifier.Bioengineering (Basel, Switzerland) · 2025Article
- Leveraging machine learning to detect subclinical seizures in Dravet syndrome: a step toward early diagnosis and SUDEP prevention.Annals of medicine and surgery (2012) · 2025Article
- Assistive Artificial Intelligence in Epilepsy and Its Impact on Epilepsy Care in Low- and Middle-Income Countries.Brain sciences · 2025Review
- Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model.Diagnostics (Basel, Switzerland) · 2025Article
- Neural network-based method for measuring the impacts of epileptic brain activities on cardiac cycles.Frontiers in neurology · 2025Article
- Advancing epileptic seizure recognition through bidirectional LSTM networks.Frontiers in computational neuroscience · 2025Article
- A deep hybrid CSAE-GRU framework with two-stage balancing for automatic epileptic seizure detection using EEG-derived features.Frontiers in 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
- Spiking neural networks for biomedical signal analysis.Biomedical engineering letters · 2024Review
- A scheme combining feature fusion and hybrid deep learning models for epileptic seizure detection and prediction.Scientific reports · 2024Article
- Epileptic seizure detection using CHB-MIT dataset: The overlooked perspectives.Royal Society open science · 2024Article
- Biomechanical Effects of Seizures on Cerebral Dynamics and Brain Stress.Brain sciences · 2024Article
- Automatic epileptic seizure detection based on persistent homology.Frontiers in physiology · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Epileptic seizure is a neurological condition caused by short and unexpectedly occurring electrical disruptions in the brain. It is estimated that roughly 60 million individuals worldwide have had an epileptic seizure. Experiencing an epileptic seizure can have serious consequences for the patient. Automatic seizure detection on electroencephalogram (EEG) recordings is essential due to the irregular and unpredictable nature of seizures. By thoroughly analyzing EEG records, neurophysiologists can discover important information and patterns, and proper and timely treatments can be provided for the patients. This research presents a novel machine learning-based approach for detecting epileptic seizures in EEG signals. A public EEG dataset from the University of Bonn was used to validate the approach. Meaningful statistical features were extracted from the original data using discrete wavelet transform analysis, then the relevant features were selected using feature selection based on the binary particle swarm optimizer. This facilitated the reduction of 75% data dimensionality and 47% computational time, which eventually sped up the classification process. After having been selected, relevant features were used to train different machine learning models, then hyperparameter optimization was utilized to further enhance the models' performance. The results achieved up to 98.4% accuracy and showed that the proposed method was very effective and practical in detecting seizure presence in EEG signals. In clinical applications, this method could help relieve the suffering of epilepsy patients and alleviate the workload of neurologists.
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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.