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New feature extraction approach for epileptic EEG signal detection using time-frequency distributions

Carlos Guerrero-Mosquera, Armando Malanda Trigueros, Jorge Iriarte Franco, Ángel Navia-Vázquez
2010 Medical and Biological Engineering and Computing  
This paper describes a new method to identify seizures in electroencephalogram (EEG) signals using feature extraction in time frequency distributions (TFDs).  ...  that the proposed scheme generalizes well and is a suitable approach for automatic seizure detection at a moderate cost, also opening the possibility of formulating new criteria to detect, classify or  ...  Discussion and conclusions A new feature extraction method in epileptic EEG signals relying on track extraction and analysis in a time frequency plane is presented.  ... 
doi:10.1007/s11517-010-0590-5 pmid:20217264 fatcat:hdmpnoidjrfhhmfych62qw3ei4

New approach in features extraction for EEG signal detection

C. Guerrero-Mosquera, A.N. Vazquez
2009 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society  
This paper describes a new approach in features extraction using time-frequency distributions (TFDs) for detecting epileptic seizures to identify abnormalities in electroencephalogram (EEG).  ...  The results show that our extraction method is a suitable approach for automatic seizure detection, and opens the possibility of formulating new criteria to detect and analyze abnormal EEGs.  ...  CONCLUSIONS AND FUTURE WORKS A new approach to identify abnormal discharges in epileptic EEG signals using track extraction is presented.  ... 
doi:10.1109/iembs.2009.5332434 pmid:19963450 fatcat:vi5nxpchxbaxrloosgs4b2cnaa

Multiresolution Analysis in EEG Signal Feature Engineering for Epileptic Seizure Detection

John Martin, Sujatha S., Swapna S.
2018 International Journal of Computer Applications  
The implementation results are placed in the form of feature distribution diagrams and provide clear indications in feature selection for epilepsy seizure detection through classification.  ...  In biomedical engineering, many attempts are being reported over the years for automated diagnosis of various brain disorders by classifying EEG (Electroencephalography) signals.  ...  ACKNOWLEDGMENT We gratefully acknowledge the Department of Epileptology at the University Hospital of Bonn for providing public access to their EEG database.  ... 
doi:10.5120/ijca2018916385 fatcat:lakuy2zfuzf4taimtiwalsy3bi

A Recent Investigation on Detection and Classification of Epileptic Seizure Techniques Using EEG Signal

Sani Saminu, Guizhi Xu, Zhang Shuai, Isselmou Abd El Kader, Adamu Halilu Jabire, Yusuf Kola Ahmed, Ibrahim Abdullahi Karaye, Isah Salim Ahmad
2021 Brain Sciences  
The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical  ...  The success of these applications largely depends on the accuracy of the detection and classification techniques employed.  ...  Therefore, features that are calculated and extracted on these signals are called time domain features, although time domain features are not mostly used alone in EEG epileptic signal analysis.  ... 
doi:10.3390/brainsci11050668 pmid:34065473 fatcat:7ttq7opwgrb4tihs3doqkeoe44

Epileptic Seizure Detection using Deep Learning Approach

Sirwan Tofiq Jaafar, Mokhtar Mohammadi
2019 UHD Journal of Science and Technology  
Most of them rely on the features extracted in the time, frequency, or time-frequency domains.  ...  An epileptic seizure is a sign of abnormal activity in the human brain. Electroencephalogram (EEG) is a standard tool that has been used vastly for detection of seizure activities.  ...  Sharif and Jafari [22] proposed a new approach in automatic seizure prediction using Poincare plane and fuzzy rules for feature extraction depends on the frequency distribution of fuzzy rules.  ... 
doi:10.21928/uhdjst.v3n2y2019.pp41-50 fatcat:rupuw2sufbehncguhyp4aacvfe

Seizure Detection Based on Adaptive Feature Extraction by Applying Extreme Learning Machines

Muhammet Baykara, Awf Abdulrahman
2021 Traitement du signal  
In this study, we performed machine learning-based and signal processing methods to detect epileptic signals.  ...  Our proposed method consists of three stages which are preprocessing, feature extraction, and classification. In the preprocessing phase, EEG signals normalized to scale all samples into [0,1] range.  ...  A new approach for epileptic seizure detection: Sample entropy based feature extraction and extreme learning machine.  ... 
doi:10.18280/ts.380210 fatcat:z3u2tlshl5c73gyw5lvjotczr4

Detecting Epileptic Seizures in EEG Signals with Complementary Ensemble Empirical Mode Decomposition and Extreme Gradient Boosting

Wu, Zhou, Li
2020 Entropy  
An electroencephalogram (EEG) records neural activity, and it is commonly used for the diagnosis of epilepsy.  ...  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.  ...  For the purpose of better detecting epileptic seizures using EEG signals, this paper proposed a novel epileptic seizure detection approach integrating CEEMD and XGBoost.  ... 
doi:10.3390/e22020140 pmid:33285915 pmcid:PMC7516550 fatcat:pgutkpnmljdszfjalja26cmh6i

DETERMINATION OF EPILEPTIC DISORDER WITH DISCRETE WAVELET TRANSFORMS AND NEURAL NETWORK CLASSIFIER

S. T. Sadish Kumar, N. Kasthuri
2014 Journal of Computer Science  
This study also discusses epilepsy disorder detection technique using neural network classifier with great accuracy.  ...  The EEG classification has also been done by back propagation algorithm in DWT. Through back propagation algorithm in wavelet transform the EEG signal divided into sub bands.  ...  Guo et al. (2010b) discusses an approach using line length features extracted using wavelet transform multi resolution decomposition and uses ANN for the classification of EEG signals.  ... 
doi:10.3844/jcssp.2014.66.72 fatcat:zziju5ahw5heflz3avamg3gvga

Epileptic Seizure Diagnosis Using EEG Signals

Shahad Saad Alwain, Naji Mutar Sahib
2020 International journal of computer science and mobile computing  
Also, improve surrogate data technique are used for feature extractions that depend on Fourier transforms (FT).  ...  The framework relies on identifying (EEG) signals for use in linear and non-linear applications.  ...  The OAT method are used for identifying the analogue EEG signals and a LMT method are used for extracting statistical features from the EEG signals was used to detect epilepsy.  ... 
doi:10.47760/ijcsmc.2020.v09i10.002 fatcat:q3lmbh444nhmrpzvxubbt6isty

Review of Machine and Deep Learning Techniques in Epileptic Seizure Detection using Physiological Signals and Sentiment Analysis

Deba Prasad Dash, Maheshkumar H Kolekar, Chinmay Chakraborty, Mohammad R. Khosravi
2022 ACM Transactions on Asian and Low-Resource Language Information Processing  
The effectiveness of time-domain features, frequency domain features, and time-frequency domain features are discussed along with different machine learning techniques.  ...  Different algorithms were proposed for efficient seizure detection using intracranial and surface EEG signals.  ...  Time Domain Features In state-of-the-art methods, extracted features from the EEG signal used for seizure and nonseizure EEG signal classiication.  ... 
doi:10.1145/3552512 fatcat:oxi2d4zoivdkzfm776jvat6rve

Classification of epileptic EEG signals based on simple random sampling and sequential feature selection

Hadi Ratham Al Ghayab, Yan Li, Shahab Abdulla, Mohammed Diykh, Xiangkui Wan
2016 Brain Informatics  
Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals.  ...  This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points.  ...  This study develops a new structure for classifying epileptic EEG signals, as presented in Fig. 1 .  ... 
doi:10.1007/s40708-016-0039-1 pmid:27747606 pmcid:PMC4883170 fatcat:gvjslil5l5fllnido7bipxhymm

Automated Epileptic Seizure Detection in Pediatric Subjects of CHB-MIT EEG Database—A Survey

J. Prasanna, M. S. P. Subathra, Mazin Abed Mohammed, Robertas Damaševičius, Nanjappan Jothiraj Sairamya, S. Thomas George
2021 Journal of Personalized Medicine  
This review paper focuses on the features of four domains, such as time, frequency, time-frequency, and nonlinear features, extracted from the EEG records, which were fed into several classifiers to classify  ...  EEG signals, thus summarizing the existing body of knowledge and opening up an enormous research area for biomedical engineers.  ...  An automatic mobile-based approach for seizure detection was proposed by analyzing EEG signals in the time domain, frequency domain, and time-frequency domain.  ... 
doi:10.3390/jpm11101028 pmid:34683169 fatcat:6hoqpkfzerbnzla7xfvznbgatq

An Efficient Method for Epileptic Seizure Detection in Long-Term EEG Recordings

Alaa Eldeen Mahmoud Helal, Ahmed Farag Seddik, Mohammed Ali Eldosoky, Ayat Allah Farouk Hussein
2014 Journal of Biomedical Science and Engineering  
The present study proposes a method to detect three epileptic spike types in EEG recordings based mainly on Template Matching Algorithm including multiple signal-processing approaches.  ...  The promising results illuminate that hybrid processing approaches in temporal, frequency and spatial domains can be a real solution to identify fast EEG transients.  ...  Yadav, et al., proposed a new algorithm for seizure detection using frequency-weighted energy.  ... 
doi:10.4236/jbise.2014.712093 fatcat:pmkm5vh7t5aanfulfbf3isbhuq

Detection and Classification Methods for EEG Epileptic Seizures

Sreelekha Panda, Raajdhani Engineering College, Bhubaneswar, India
2019 International Journal of Advanced Trends in Computer Science and Engineering  
Time frequency transforms and system learning plays an important function in extracting meaningful facts.  ...  This paper provides an overview of detection and classification era for the reason of EEG seizure.  ...  Feature extraction Feature extraction from the EEG signals have mostly amplitude-based capabilities in time domain, time-frequency distribution provides Instantaneous Frequency based functions [15] .  ... 
doi:10.30534/ijatcse/2019/40862019 fatcat:hp6rng5rvzcrdfodvud5okgeo4

Bio-Signal Complexity Analysis in Epileptic Seizure Monitoring: A Topic Review

Zhenning Mei, Xian Zhao, Hongyu Chen, Wei Chen
2018 Sensors  
Despite the promising results about epileptic seizure detection and prediction through offline analysis, we are still lacking robust, tried-and-true real-time applications.  ...  By delving into the complexity in electrophysiological signals and neuroimaging, new insights have emerged.  ...  Trade-offs should be made depending on the specific requirements for time and frequency resolutions. Time-frequency analysis provides a new idea for feature extraction. Tzallas et al.  ... 
doi:10.3390/s18061720 pmid:29861451 pmcid:PMC6022076 fatcat:owwowgqp7za5ll5ph5lrpiysma
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