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New feature extraction approach for epileptic EEG signal detection using time-frequency distributions
2010
Medical and Biological Engineering and Computing
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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
2009
2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society
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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
2018
International Journal of Computer Applications
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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
2021
Brain Sciences
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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
2019
UHD Journal of Science and Technology
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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
2021
Traitement du signal
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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
2020
Entropy
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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
2014
Journal of Computer Science
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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
2020
International journal of computer science and mobile computing
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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
2022
ACM Transactions on Asian and Low-Resource Language Information Processing
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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
2016
Brain Informatics
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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
2021
Journal of Personalized Medicine
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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
2014
Journal of Biomedical Science and Engineering
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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
2019
International Journal of Advanced Trends in Computer Science and Engineering
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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
2018
Sensors
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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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