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Combined neural network model employing wavelet coefficients for EEG signals classification

Author:

Elif Derya ÜbeyliAuthors Info & Claims

Digital Signal Processing, Volume 19, Issue 2

Pages 297 - 308

https://doi.org/10.1016/j.dsp.2008.07.004

Published: 01 March 2009 Publication History

Abstract

This paper illustrates the use of combined neural network model to guide model selection for classification of electroencephalogram (EEG) signals. The EEG signals were decomposed into time-frequency representations using discrete wavelet transform and statistical features were calculated to depict their distribution. The first-level networks were implemented for the EEG signals classification using the statistical features as inputs. To improve diagnostic accuracy, the second-level networks were trained using the outputs of the first-level networks as input data. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified with the accuracy of 94.83% by the combined neural network. The combined neural network model achieved accuracy rates which were higher than that of the stand-alone neural network model.

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Cited By

Tan QXu XLiang H(2023)Physiological Big Data Mining Through Machine Learning and Wireless Sensor NetworksInternational Journal of Distributed Systems and Technologies10.4018/IJDST.31794214:2(1-12)Online publication date: 10-Feb-2023
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Bergil EOral CErgül E(2023)Classification of arithmetic mental task performances using EEG and ECG signalsThe Journal of Supercomputing10.1007/s11227-023-05294-079:14(15535-15547)Online publication date: 19-Apr-2023
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Bird JLotfi A(2023)Fall compensation detection from EEG using neuroevolution and genetic hyperparameter optimisationGenetic Programming and Evolvable Machines10.1007/s10710-023-09453-324:1Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1007/s10710-023-09453-3
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Information

Published In

cover image Digital Signal Processing

Digital Signal Processing Volume 19, Issue 2

March, 2009

186 pages

ISSN:1051-2004

Issue’s Table of Contents

Copyright © Elsevier Inc. © 2008.

Publisher

Academic Press, Inc.

United States

Publication History

Published: 01 March 2009

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Cited By

Tan QXu XLiang H(2023)Physiological Big Data Mining Through Machine Learning and Wireless Sensor NetworksInternational Journal of Distributed Systems and Technologies10.4018/IJDST.31794214:2(1-12)Online publication date: 10-Feb-2023
https://dl.acm.org/doi/10.4018/IJDST.317942
Bergil EOral CErgül E(2023)Classification of arithmetic mental task performances using EEG and ECG signalsThe Journal of Supercomputing10.1007/s11227-023-05294-079:14(15535-15547)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1007/s11227-023-05294-0
Bird JLotfi A(2023)Fall compensation detection from EEG using neuroevolution and genetic hyperparameter optimisationGenetic Programming and Evolvable Machines10.1007/s10710-023-09453-324:1Online publication date: 17-May-2023
https://dl.acm.org/doi/10.1007/s10710-023-09453-3
Bird JMakedon F(2022)EEG Wavelet Classification for Fall Detection with Genetic ProgrammingProceedings of the 15th International Conference on PErvasive Technologies Related to Assistive Environments10.1145/3529190.3535339(376-382)Online publication date: 29-Jun-2022
https://dl.acm.org/doi/10.1145/3529190.3535339
El Ouassif BIdri AHosni M(2021)Investigating Accuracy and Diversity in Heterogeneous Ensembles for Breast Cancer ClassificationComputational Science and Its Applications – ICCSA 202110.1007/978-3-030-86960-1_19(263-281)Online publication date: 13-Sep-2021
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Kaur BSingh DRoy P(2019)Age and gender classification using brain–computer interfaceNeural Computing and Applications10.1007/s00521-018-3397-131:10(5887-5900)Online publication date: 1-Oct-2019
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Pereira LPapa JCoelho ALima CPereira DAlbuquerque V(2019)Automatic identification of epileptic EEG signals through binary magnetic optimization algorithmsNeural Computing and Applications10.1007/s00521-017-3124-331:2(1317-1329)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s00521-017-3124-3
Pan YLi HLiu LLi QHou XDong B(2019)aEEG Signal Analysis with Ensemble Learning for Newborn Seizure DetectionMultiscale Multimodal Medical Imaging10.1007/978-3-030-37969-8_10(76-84)Online publication date: 13-Oct-2019
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Hong SYin J(2018)Remaining Useful Life Prediction of Bearing Based on Deep Perceptron Neural NetworksProceedings of the 2018 2nd International Conference on Big Data and Internet of Things10.1145/3289430.3289438(175-179)Online publication date: 24-Oct-2018
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