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Evaluation of Feature Selection Methods for Classification of Epileptic Seizure EEG Signals

Sergio E. Sánchez-Hernández, Ricardo A. Salido-Ruiz, Sulema Torres-Ramos, Israel Román-Godínez
2022 Sensors  
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

N. Sriraam, S. Raghu, Kadeeja Tamanna, Leena Narayan, Mehraj Khanum, A. S. Hegde, Anjani Bhushan Kumar
2018 Brain Informatics  
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

Aayesha, Muhammad Bilal Qureshi, Muhammad Afzaal, Muhammad Shuaib Qureshi, Jeonghwan Gwak
2022 Computers Materials & Continua  
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

Md. Khademul Islam Molla, Kazi Mahmudul Hassan, Md. Rabiul Islam, Toshihisa Tanaka
2020 Sensors  
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

Mona Ali, Mohamed Abd-Elfattah
2018 International Journal of Intelligent Engineering and Systems  
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

Wu, Zhou, Li
2020 Entropy  
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

Enamul Kabir, Siuly, Yanchun Zhang
2016 Brain Informatics  
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

Sang-Hong Lee
2021 Technology and Health Care  
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

Shahab Abdulla, Mohammed Diykh, Sarmad K. D. Alkhafaji, Jonathan H. Greena, Hanan Al-Hadeethi, Atheer Y. Oudah, Haydar Abdulameer Marhoon
2021 Diagnostics  
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

Guangda Liu, Ruolan Xiao, Lanyu Xu, Jing Cai
2021 Frontiers in Systems Neuroscience  
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

Vibha Patel, Jaishree Tailor, Amit Ganatra
2021 Open Biomedical Engineering Journal  
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

Yanan Lu, Yu Ma, Chen Chen, Yuanyuan Wang, Carlos Gómez, Severin P. Schwarzacher, Huiyu Zhou
2018 Technology and Health Care  
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


Yuna Sugianela, Qonita Luthfia Sutino, Darlis Herumurti
2018 Jurnal Ilmu Komputer dan Informasi  
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

Yinda Zhang, Shuhan Yang, Yang Liu, Yexian Zhang, Bingfeng Han, Fengfeng Zhou
2018 Sensors  
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

Yunyuan Gao, Bo Gao, Qiang Chen, Jia Liu, Yingchun Zhang
2020 Frontiers in Neurology  
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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