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Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals
2022
Sensors
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The comparison was implemented for different feature sets and the number of features. Furthermore, the similarity between selected feature subsets across classification models was evaluated. ...
In this study, the performance of a set of feature selection methods was compared across different classification models; the classification task consisted of the detection of ictal activity from the CHB-MIT ...
en registros EEG de crisis epilépticas utilizando métodos de inteligencia artificial explicable" by the Data Analysis and Supercomputing Center (CADS, for its acronym in Spanish) of the University of ...
doi:10.3390/s22083066
pmid:35459052
pmcid:PMC9031940
fatcat:miqgthu7cbhytgijysbgr4dkku
Automated epileptic seizures detection using multi-features and multilayer perceptron neural network
2018
Brain Informatics
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Detection of epileptic seizure activities from long-term multi-channel electroencephalogram (EEG) signals plays a significant role in the timely treatment of the patients with epilepsy. ...
Visual identification of epileptic seizure in long-term EEG is cumbersome and tedious for neurologists, which might also lead to human error. ...
Acknowledgements The authors would like to acknowledge the doctors of the Institute of Neurosciences, Ramaiah Memorial College and Hospital, Bengaluru, India, for permitting to use the EEG data for research ...
doi:10.1186/s40708-018-0088-8
pmid:30175391
pmcid:PMC6170940
fatcat:rtdehyoa5jhipdeec7f2eqjjdq
Fuzzy-Based Automatic Epileptic Seizure Detection Framework
2022
Computers Materials & Continua
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It applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic seizures. ...
Some characterizing features of epileptic and non-epileptic EEG signals overlap; therefore, it requires that analysis of signals must be performed from diverse perspectives. ...
[22] introduced a method for seizure vs. non-seizure classification of EEG signals. ...
doi:10.32604/cmc.2022.020348
fatcat:yqsfnkzy3jeflgijk3hfboc4py
Graph Eigen Decomposition-Based Feature-Selection Method for Epileptic Seizure Detection Using Electroencephalography
2020
Sensors
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The selected features are used to classify seizure and nonseizure EEG signals using a feedforward neural network (FfNN). ...
Hence, the proposed method is confirmed as a potential marker for EEG-based seizure detection. ...
In this study, FfNN is used for the classification of epileptic and nonepileptic EEG signals. ...
doi:10.3390/s20164639
pmid:32824708
fatcat:oqipcsso7rfvfordu2tcmiotjy
A Hybrid Model (SVM-LOA) for Epileptic Seizure Detection in Long-Term EEG Records Using Machine Learning Techniques
2018
International Journal of Intelligent Engineering and Systems
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of support vector machines (SVMs) for classification of Electroencephalogram (EEG) signals . ...
Therefore; the SVM-LOA is an efficient model for neuroscientists to detect epileptic seizure in EEG. ...
The suggested classification method, involves four phases: 1) Pre-processing used to get rid of the noises from the EEG signals. 2) Feature extraction used to extract the EEG signal features from decomposed ...
doi:10.22266/ijies2018.1031.15
fatcat:jvpfjpzwnzdsnajjtjwj27ys74
Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting
2020
Entropy
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Secondly, the multi-domain features were extracted from raw signals and the decomposed components, and they were further selected according to the importance scores of the extracted features. ...
The extensive experimental results indicated that, compared with some previous EEG classification models, CEEMD-XGBoost can significantly enhance the detection performance of epileptic seizures in terms ...
Conflicts of Interest: The authors declare no conflicts of interest. ...
doi:10.3390/e22020140
pmid:33285915
pmcid:PMC7516550
fatcat:pgutkpnmljdszfjalja26cmh6i
Epileptic seizure detection from EEG signals using logistic model trees
2016
Brain Informatics
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Then, some statistical features are extracted from these EEG signals and the obtained feature set is fed into the LMT classification model to detect epileptic seizure. ...
The proposed method outperforms some state-of-the-art methods of epileptic EEG signal detection using the same EEG dataset. ...
, and SVM for detection of epileptic seizure EEG signals (healthy, seizure-free, and seizure). ...
doi:10.1007/s40708-015-0030-2
pmid:27747604
pmcid:PMC4883168
fatcat:tgct4jwe5jbo7asfnyrlkcdhtq
Classification of epileptic seizure using feature selection based on fuzzy membership from EEG signal
2021
Technology and Health Care
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Using distance-based feature selection, 22 minimum features with a high performance result are selected, with the shortest DBCG of BSWFMs removed individually from the initial 24 features. ...
In this study, only the mean DBCG is used to select the features; in the future, however, it will be necessary to incorporate statistical methods such as the standard deviation, maximum, and normal distribution ...
Lee / Classification of epileptic seizure using feature selection based on fuzzy membership from EEG signal Fig. 1. ...
doi:10.3233/thc-218049
pmid:33682788
pmcid:PMC8158055
fatcat:2somy7jjebfazoeecbdvxrxlni
Determinant of Covariance Matrix Model Coupled with AdaBoost Classification Algorithm for EEG Seizure Detection
2021
Diagnostics
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The selected features are then fed into the proposed AdaBoost Back-Propagation neural network (AB_BP_NN) to effectively classify EEG signals into seizure and free seizure segments. ...
to construe the most pertinent classified features for each pair in the EEG signal group. ...
The proposed methodology for EEG signal analysis.
Figure 2 . 2 Figure 2. Two-stage feature selection method. ...
doi:10.3390/diagnostics12010074
pmid:35054242
pmcid:PMC8774996
fatcat:gnudauhp3fbmbclnwwnenkeuuu
Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals
2021
Frontiers in Systems Neuroscience
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This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. ...
The application of seizure prediction and localization based on EEG signals in the diagnosis of epilepsy was also introduced. ...
AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication. ...
doi:10.3389/fnsys.2021.685387
pmid:34093143
pmcid:PMC8173051
fatcat:j5xweg7bcjez5cc53ltm3ttuwe
Essentials of Predicting Epileptic Seizures Based on EEG Using Machine Learning: A Review
2021
Open Biomedical Engineering Journal
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Researchers have shown great interest in the task of epileptic seizure prediction for a few decades. ...
Results: Work here presents the essential components required for the prediction of epileptic seizures, which includes the basics of epilepsy, its treatment, and the need for seizure prediction algorithms ...
Another noticeable approach for epileptic seizure prediction is based on signal processing methods. ...
doi:10.2174/1874120702115010090
fatcat:g53gb7dbunh2zke3e7avamfnpa
Classification of single-channel EEG signals for epileptic seizures detection based on hybrid features
2018
Technology and Health Care
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With the hybrid features, EEG signals are classified and the epileptic seizures are detected. ...
METHODS: The hybrid features consist of both the ones usually used in EEG signal analysis and the Kraskov entropy based on Hilbert-Huang Transform which is proposed for the first time. ...
Acknowledgments This work was supported by the National Natural Science Foundation of China (No. 61202264).
Conflict of interest None to report. ...
doi:10.3233/thc-174679
pmid:29710759
pmcid:PMC6004942
fatcat:ie4kfapkhzg3bhmtxmococuuym
EEG CLASSIFICATION FOR EPILEPSY BASED ON WAVELET PACKET DECOMPOSITION AND RANDOM FOREST
2018
Jurnal Ilmu Komputer dan Informasi
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The challenge of study is how to develop a method for signal processing that extract the subtle information of EEG and use it for automating the detection of epileptic with high accuration, so we can use ...
In this study we developed a method to classify the EEG signal based on Wavelet Packet Decomposition that decompose the EEG signal and Random Forest for seizure detetion. ...
In this study, we select seven different statistical features for EEG classification, aiming at decreasing the dimensionality of dataset. ...
doi:10.21609/jiki.v11i1.549
fatcat:vwj5vfdxnjg35fxtltbsgimq5i
Integration of 24 Feature Types to Accurately Detect and Predict Seizures Using Scalp EEG Signals
2018
Sensors
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This study extracted a comprehensive list of 24 feature types from the scalp EEG signals and found 170 out of the 2794 features for an accurate classification of epileptic seizures. ...
An accuracy (Acc) of 99.40% was optimized for detecting epileptic seizures from the scalp EEG signals. ...
Conflicts of Interest: The authors declare no conflict of interest. Sensors 2018, 18, 1372 ...
doi:10.3390/s18051372
pmid:29710763
pmcid:PMC5982573
fatcat:nbp4a76so5d2fa4pvscrb7rmem
Deep Convolutional Neural Network-Based Epileptic Electroencephalogram (EEG) Signal Classification
2020
Frontiers in Neurology
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For instance, it achieves an average classification accuracy of over 90% in a case study with CHB-MIT epileptic EEG data. ...
A new deep learning-based classification methodology, namely epileptic EEG signal classification (EESC), is proposed in this paper. ...
This work was supported by the National Nature Science Foundation of China under Grant 61971168, the National Nature Science Foundation of China 61871427, and the Zhejiang Natural Science Foundation LY18F030009 ...
doi:10.3389/fneur.2020.00375
pmid:32528398
pmcid:PMC7257380
fatcat:o254cir4vzb6ncmi5bxjwiyfze
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