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Wavelet Transform as a Helping Tool During EEG Analysis in Children with Epilepsy

BACKGROUND: Epilepsy is a brain disorder characterised by unpredictable and excessive nerve cell activity that causes epileptic seizures. Epileptic seizures are more common in children and adolescents than in elderly population. Electroencephalography (EEG) is a diagram of electrical activity of the...

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Autores principales: Zahirovic, Salko, Avdakovic, Samir, Hadzagic-Catibusic, Feriha, Dautbasic, Nedis, Dedovic, Maja Muftić, Suljic, Enra, Sefo, Haso, Omerhodzic, Ibrahim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Academy of Medical sciences 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8443131/
https://www.ncbi.nlm.nih.gov/pubmed/34584332
http://dx.doi.org/10.5455/aim.2021.29.104-107
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author Zahirovic, Salko
Avdakovic, Samir
Hadzagic-Catibusic, Feriha
Dautbasic, Nedis
Dedovic, Maja Muftić
Suljic, Enra
Sefo, Haso
Omerhodzic, Ibrahim
author_facet Zahirovic, Salko
Avdakovic, Samir
Hadzagic-Catibusic, Feriha
Dautbasic, Nedis
Dedovic, Maja Muftić
Suljic, Enra
Sefo, Haso
Omerhodzic, Ibrahim
author_sort Zahirovic, Salko
collection PubMed
description BACKGROUND: Epilepsy is a brain disorder characterised by unpredictable and excessive nerve cell activity that causes epileptic seizures. Epileptic seizures are more common in children and adolescents than in elderly population. Electroencephalography (EEG) is a diagram of electrical activity of the brain and it is used as a method of choice for diagnosing epilepsy. Despite the accurate EEG tracing of electrical activity in the brain, the disadvantage of this type of analysing is the doctor’s skill to read the EEG correctly. OBJECTIVE: The aim of this study was ro represents further research presented in our pevious works with wavelet based EEG analysis after masuring a multiresolution as relation between time and frequency resolution. METHODS: Signal database set consist of 51 patients: a) healthy patient; b) 50 patients with a diagnosis of epilepsy. Additional characteristics of the analysed data: a) 19 signals-channels of EEG, b) Duration – 20 s or 2688 samples and. Nowadays, we can find dozens of EEG signal analysis papers using mathematical approach and with a focus on identification of epilepsy. RESULTS: This paper represents some results relating to the analysis of EEG in children using Wavelet Transform (WT). The signals was collected and analysed at the Department of neuropediatrics, Pediatric Clinic at the University Clinical Center, University of Sarajevo. CONCLUSION: Using this approach it is possible to clearly differentiate patients with a diagnosis of epilepsy from healthy ones.
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spelling pubmed-84431312021-09-27 Wavelet Transform as a Helping Tool During EEG Analysis in Children with Epilepsy Zahirovic, Salko Avdakovic, Samir Hadzagic-Catibusic, Feriha Dautbasic, Nedis Dedovic, Maja Muftić Suljic, Enra Sefo, Haso Omerhodzic, Ibrahim Acta Inform Med Original Paper BACKGROUND: Epilepsy is a brain disorder characterised by unpredictable and excessive nerve cell activity that causes epileptic seizures. Epileptic seizures are more common in children and adolescents than in elderly population. Electroencephalography (EEG) is a diagram of electrical activity of the brain and it is used as a method of choice for diagnosing epilepsy. Despite the accurate EEG tracing of electrical activity in the brain, the disadvantage of this type of analysing is the doctor’s skill to read the EEG correctly. OBJECTIVE: The aim of this study was ro represents further research presented in our pevious works with wavelet based EEG analysis after masuring a multiresolution as relation between time and frequency resolution. METHODS: Signal database set consist of 51 patients: a) healthy patient; b) 50 patients with a diagnosis of epilepsy. Additional characteristics of the analysed data: a) 19 signals-channels of EEG, b) Duration – 20 s or 2688 samples and. Nowadays, we can find dozens of EEG signal analysis papers using mathematical approach and with a focus on identification of epilepsy. RESULTS: This paper represents some results relating to the analysis of EEG in children using Wavelet Transform (WT). The signals was collected and analysed at the Department of neuropediatrics, Pediatric Clinic at the University Clinical Center, University of Sarajevo. CONCLUSION: Using this approach it is possible to clearly differentiate patients with a diagnosis of epilepsy from healthy ones. Academy of Medical sciences 2021-06 /pmc/articles/PMC8443131/ /pubmed/34584332 http://dx.doi.org/10.5455/aim.2021.29.104-107 Text en © 2021 Salko Zahirovic, Samir Avdakovic, Feriha Hadzagic-Catibusic, Nedis Dautbasic, Maja Muftic Dedovic, Enra Suljic, Haso Sefo, Ibrahim Omerhodzic https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Paper
Zahirovic, Salko
Avdakovic, Samir
Hadzagic-Catibusic, Feriha
Dautbasic, Nedis
Dedovic, Maja Muftić
Suljic, Enra
Sefo, Haso
Omerhodzic, Ibrahim
Wavelet Transform as a Helping Tool During EEG Analysis in Children with Epilepsy
title Wavelet Transform as a Helping Tool During EEG Analysis in Children with Epilepsy
title_full Wavelet Transform as a Helping Tool During EEG Analysis in Children with Epilepsy
title_fullStr Wavelet Transform as a Helping Tool During EEG Analysis in Children with Epilepsy
title_full_unstemmed Wavelet Transform as a Helping Tool During EEG Analysis in Children with Epilepsy
title_short Wavelet Transform as a Helping Tool During EEG Analysis in Children with Epilepsy
title_sort wavelet transform as a helping tool during eeg analysis in children with epilepsy
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8443131/
https://www.ncbi.nlm.nih.gov/pubmed/34584332
http://dx.doi.org/10.5455/aim.2021.29.104-107
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