Automatic feature extraction using genetic programming: An application to epileptic EEG classification.

Human experts decide which features are extracted from the signals, and used as inputs to the classification system. ... The proposed method is based on Genetic Programming and, in order to test this method, it has been applied to a well-known EEG database related to epilepsy, a disease suffered by millions of people. ... The method proposed in this paper uses Genetic Programming (GP) in order to perform the automatic feature extraction and the classification in one single step. ...

doi:10.1145/2739482.2768507 dblp:conf/gecco/RiveroFDP15 fatcat:pocmtlouwvf2na5pbmbp6f4nqy

Multiple Versions

We use genetic algorithm for choosing the training set and also implement a post-classification stage using harmonic weights to increase the accuracy. ... We use wavelet transform for feature extraction and obtain statistical parameters from the decomposed wavelet coefficients. ... , Freiburg, Germany for permitting us to use the EEG data from their website. ...

doi:10.1016/j.cmpb.2008.02.005 pmid:18406490 fatcat:keqciixzbbhuhnwseuqtrgtaby

The proposed method is comprised of three stages: preprocessing, genetic programming-based feature generation, and classification. ... This paper explores the use of eight statistical features and genetic programing (GP) with the K-nearest neighbor (KNN) for interictal spike detection. ... Acknowledgments The authors would like to acknowledge the support received from King Fahad Medical City (KFMC), Kingdom of Saudi Arabia. ...

doi:10.1155/2017/3035606 pmid:29118962 pmcid:PMC5651155 fatcat:ttc46chvebdyvd6fb3jxqmzy3a

DOAJ

of Feature Extraction Using Wavelet Decomposition Sathya Costagliola, Bernardo Dal Seno and Matteo Matteucci P149 On Classifiability of Wavelet Features for EEG-Based Brain-Computer Interfaces Jesse ... Brain and Peripheral Signals: Using Correlation Dimension to Improve the Results of EEG Zahra Khalili and Mohammad Hasan Moradi P148 Recognition and Classification of P300s in EEG Signals by Means ...

doi:10.1109/ijcnn.2009.5178575 fatcat:kxaceopferd23ps5uyrn3m7xjy

Therefore, Genetic Programming (GP), a population based meta-heuristic for automatic program induction, is used to automatically search for the mapping functions. ... Moreover, a proof-of-concept real-world prototype is presented, where a GP classifier is transferred and hard-coded on an embedded system using a digital-to-analogue converter and a field programmable ... Thanks are extended to Francisco Sancho from Hospital Universitario de Valencia, for his collaboration and support during the signal recording process. ...

doi:10.1016/j.compbiomed.2013.08.016 pmid:24209917 fatcat:3syidaumxjf4neis2vh4gfnlgi

This review presents an overview on the most suitable methods in terms of preprocessing, feature extraction, feature selection and classifier adopted to precisely discriminate the sleep stages. ... Feature extraction and classification schemes are assessed in terms of accuracy and robustness against noise. ... The present review is an attempt to discuss the most recent automatic sleep stage classification methods using singlechannel EEG data. ...

doi:10.1016/j.cmpb.2016.12.004 pmid:28254093 fatcat:uoosippv2neerhh3xjgjyz54ca

Here, we propose an enhanced feature extraction algorithm (Augmented Feature Extraction with Genetic Programming, or +FEGP) that improves upon previous results by employing a Genetic-Programming-based ... The design of efficient electroencephalogram (EEG) classification systems for the detection of mental states is still an open problem. ... The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ...

doi:10.3390/a13090221 fatcat:ulfbozz5trc23f5qldnz2v3aay

DOAJ Szczepanski

This minireview summarized the latest research of epilepsy detection techniques that focused on acquiring, preprocessing, feature extraction, and classification of epileptic EEG signals. ... New algorithms are continuously introduced to shorten the detection time and improve classification accuracy. ... 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

DOAJ

Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal ... feature selection or/and classification. ... Acknowledgments Grant funds from the United Negro College Fund Special Programs Corporation NASA Harriett G. Jenkins Predoctoral Fellowship Program to Dr. ...

doi:10.1016/j.engappai.2014.12.008 pmid:25580059 pmcid:PMC4285716 fatcat:ilwjladvfrfadmw3zg2sqhaa3i

In the next step, a novel method using genetic programming (GP) combined with support vector machine (SVM) confusion matrix as fitness function weight is used to extract feature vectors compressed into ... The most important part of signal processing for classification is feature extraction as a mapping from original input electroencephalographic (EEG) data space to new features space with the biggest class ... Feature extraction and compression based on genetic programming (GP)-support vector machine (SVM) algorithm 4. Final stage SVM classification. ...

doi:10.2147/ndt.s167841 pmid:30275697 pmcid:PMC6157576 fatcat:e7eayvivbzgbvhbhrlya65y47q

DOAJ

classification and parking assistance will be presented. ... The wireless transmitters and RF PA design require several new considerations to be useful for New Generation Radio Access Network (NG-RAN) in 5 G applications. ... played a crucial role in classifying epileptic seizures due to its capability of automatically learning the discriminatory features from the raw electroencephalogram (EEG) data. ...

doi:10.1109/ncc52529.2021.9530194 fatcat:ahdw5ezvtrh4nb47l2qeos3dwq

There have been many methods for EEG analysis using supervised and unsupervised machine learning techniques. ... In addition, compared with the other supervised methods, the proposed method was able to significantly improve the accuracy of the EEG prediction. ... A conceptual system based on ML was proposed by [33] to represent an automatic identification of EEG artifacts in three subjects: canine, rodent, and people. ...

doi:10.1155/2022/4439189 fatcat:3hvwxfryzzaftjz4ap6zip7u2q

DOAJ

A recent strategy for SI is to analyze Speech Related Potentials (SRP) features on EEG signals to recognize vowels. ... Three main techniques were applied to identify the morphological features from EEG signals in order to evaluate recordings without having to train a model using a database: Continuous Wavelet Transform ...

doi:10.1109/cce50788.2020.9299182 fatcat:dff7ylnwrzabdcv276gbwcgkji

The signals have been processed by CNN for feature extraction from runtime environment while LSTM has used for classification of entire data. ... In this research to describes a state of art for effective epileptic disease detection prediction and classification using hybrid deep learning algorithms. ... with Fourier Expansions, a Lifts, in [3] , features were extracted with the FFT, and classification trees were used to analyse these features PCA review found that these two methods to be applicable ...

doi:10.3233/apc210241 fatcat:dp4b5uv2ozf2zoaqttgf2pveui

Open Access

Statistical features are extracted from the EEG signal and are passed to a modified Support Vector Machine (SVM) algorithm for classification. ... The EEG signal can be recorded either from the scalp or invasively from the cortex using intracranial electrodes. ... Automatic feature extraction using genetic programming: An application to epileptic EEG classification. Expert Syst. Appl., 38(8): 1042. Homan, R.W., J. Herman and P. Purdy, 1987. ...

doi:10.19026/rjms.9.5066 fatcat:g2wg2c43azfmbftiisrbtkzhq4

Szczepanski

Classification of Two-channel Signals by Means of Genetic Programming

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Epileptic EEG detection using neural networks and post-classification

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Epileptic MEG Spike Detection Using Statistical Features and Genetic Programming with KNN

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Program

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Identification of epilepsy stages from ECoG using genetic programming classifiers

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A comparative review on sleep stage classification methods in patients and healthy individuals

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EEG Feature Extraction Using Genetic Programming for the Classification of Mental States

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Minireview of Epilepsy Detection Techniques Based on Electroencephalogram Signals

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Genetic programming and frequent itemset mining to identify feature selection patterns of iEEG and fMRI epilepsy data

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Automatic epilepsy detection using fractal dimensions segmentation and GP-SVM classification

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Program

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Eye State Identification Utilizing EEG Signals: A Combined Method Using Self-Organizing Map and Deep Belief Network

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Final Program

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A Review on BCI Emotions Classification for EEG Signals Using Deep Learning [chapter]

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Epileptic Seizure Detection in EEG using Support Vector Machines and Statistical Analysis

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