ArticleSensors (Basel, Switzerland)2019
Automated Recognition of Epileptic EEG States Using a Combination of Symlet Wavelet Processing, Gradient Boosting Machine, and Grid Search Optimizer.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Research on fall prediction in elderly patients with chronic diseases based on explainable machine learning: an aging perspective.BMC geriatrics · 2026Article
- Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier.Scientific reports · 2024Article
- Robust Epileptic Seizure Detection Using Long Short-Term Memory and Feature Fusion of Compressed Time-Frequency EEG Images.Sensors (Basel, Switzerland) · 2023Article
- Investigating population-specific epilepsy detection from noisy EEG signals using deep-learning models.Heliyon · 2023Article
- EEG-based epileptic seizure detection using binary dragonfly algorithm and deep neural network.Scientific reports · 2023Article
- Automated recognition of epilepsy from EEG signals using a combining space-time algorithm of CNN-LSTM.Scientific reports · 2023Article
- A Shallow Autoencoder Framework for Epileptic Seizure Detection in EEG Signals.Sensors (Basel, Switzerland) · 2023Article
- Overview of methods and available tools used in complex brain disorders.Open research Europe · 2023Review
- Machine learning and deep learning approach for medical image analysis: diagnosis to detection.Multimedia tools and applications · 2022Article
- Automatic Detection of Epilepsy Based on Entropy Feature Fusion and Convolutional Neural Network.Oxidative medicine and cellular longevity · 2022Article
- Synergy Factorized Bilinear Network with a Dual Suppression Strategy for Brain Tumor Classification in MRI.Micromachines · 2021Article
- Level-K Classification from EEG Signals Using Transfer Learning.Sensors (Basel, Switzerland) · 2021Article
- A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal.Brain sciences · 2021Review
- Combining Inter-Subject Modeling with a Subject-Based Data Transformation to Improve Affect Recognition from EEG Signals.Sensors (Basel, Switzerland) · 2019Article
- Epilepsy Detection by Using Scalogram Based Convolutional Neural Network from EEG Signals.Brain sciences · 2019Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Automatic recognition methods for non-stationary electroencephalogram (EEG) data collected from EEG sensors play an essential role in neurological detection. The integrated approaches proposed in this study consist of Symlet wavelet processing, a gradient boosting machine, and a grid search optimizer for a three-class classification scheme for normal subjects, intermittent epilepsy, and continuous epilepsy. Fourth-order Symlet wavelets are adopted to decompose the EEG data into five frequencies sub-bands, such as gamma, beta, alpha, theta, and delta, whose statistical features were computed and used as classification features. The grid search optimizer is used to automatically find the optimal parameters for training the classifier. The classification accuracy of the gradient boosting machine was compared with that of a conventional support vector machine and a random forest classifier constructed according to previous descriptions. Multiple performance indices were used to evaluate the proposed classification scheme, which provided better classification accuracy and detection effectiveness than has been recently reported in other studies on three-class classification of EEG data.
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