Publicação
Classification of electroencephalogram signals using artificial neural networks
| Resumo: | The study of Artificial Neural Networks (ANN) has proved to be fascinating over the years and the development of these networks has grown strongly in recent years. The neural networks have come to be increasingly convincing methods for solving complex problems, through artificial intelligence. In particular this work focused on development of an artificial neural network for identifying diseases: Parkinson's, Huntington's and Amyotrophic Lateral Sclerosis, based on signals from the Electroencephalogram (EEG). The phases of the project were developed through a number of operations implemented in Matlab. The Fourier transform was seen as the main technique of signal processing, in order to analyze and diagnose diseases in the study. The work consisted in the first stage process the EEG signals to serve as an entry into the ANN in order to reveal a distinctive feature in the different diseases studied, and then, create a model capable to distinguish the diseases. For this purpose 4 methodologies were used with different processing of the EEG signal. The 4 methodologies are compared in this paper. |
|---|---|
| Autores principais: | Rodrigues, Pedro Miguel |
| Outros Autores: | Teixeira, João Paulo |
| Assunto: | Artificial neural networks FFT EEG Classification |
| Ano: | 2010 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Politécnica de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital da UPB |
| _version_ | 1877782610923487232 |
|---|---|
| author | Rodrigues, Pedro Miguel |
| author2 | Teixeira, João Paulo |
| author2_role | author |
| author_facet | Rodrigues, Pedro Miguel Teixeira, João Paulo |
| author_role | author |
| contributor_name_str_mv | Biblioteca Digital da UPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Rodrigues, Pedro Miguel\"},{\"Person.name\":\"Teixeira, João Paulo\",\"Person.identifier.cienciaID\":\"4F15-B322-59B4\",\"Person.identifier.orcid\":\"0000-0002-6679-5702\",\"Person.identifier.researcherID\":\"N-6576-2013\",\"Person.identifier.scopusAuthorID\":\"57069567500\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital da UPB |
| datacite.creators.creator.creatorName.fl_str_mv | Rodrigues, Pedro Miguel Teixeira, João Paulo |
| datacite.date.Accepted.fl_str_mv | 2010-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2011-05-20T09:33:29Z |
| datacite.date.embargoed.fl_str_mv | 2011-05-20T09:33:29Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Artificial neural networks FFT EEG Classification |
| datacite.titles.title.fl_str_mv | Classification of electroencephalogram signals using artificial neural networks |
| dc.contributor.none.fl_str_mv | Biblioteca Digital da UPB |
| dc.creator.none.fl_str_mv | Rodrigues, Pedro Miguel Teixeira, João Paulo |
| dc.date.Accepted.fl_str_mv | 2010-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2011-05-20T09:33:29Z |
| dc.date.embargoed.fl_str_mv | 2011-05-20T09:33:29Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/4421 |
| dc.language.none.fl_str_mv | eng |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| dc.subject.none.fl_str_mv | Artificial neural networks FFT EEG Classification |
| dc.title.fl_str_mv | Classification of electroencephalogram signals using artificial neural networks |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_5794 |
| description | The study of Artificial Neural Networks (ANN) has proved to be fascinating over the years and the development of these networks has grown strongly in recent years. The neural networks have come to be increasingly convincing methods for solving complex problems, through artificial intelligence. In particular this work focused on development of an artificial neural network for identifying diseases: Parkinson's, Huntington's and Amyotrophic Lateral Sclerosis, based on signals from the Electroencephalogram (EEG). The phases of the project were developed through a number of operations implemented in Matlab. The Fourier transform was seen as the main technique of signal processing, in order to analyze and diagnose diseases in the study. The work consisted in the first stage process the EEG signals to serve as an entry into the ANN in order to reveal a distinctive feature in the different diseases studied, and then, create a model capable to distinguish the diseases. For this purpose 4 methodologies were used with different processing of the EEG signal. The 4 methodologies are compared in this paper. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | conferencePaper |
| fulltext.url.fl_str_mv | https://bibliotecadigital.unipb.pt/bitstreams/caa8456e-ee88-4e35-a628-a82287091338/download |
| id | ipb_99ea8891662e3de7693f0c3df76d11ee |
| identifier.url.fl_str_mv | http://hdl.handle.net/10198/4421 |
| inst_facet_str | urn:organizationAcronym:unipb{{{_:::_}}}Universidade Politécnica de Bragança |
| instacron_str | unipb |
| institution | Universidade Politécnica de Bragança |
| instname_str | Universidade Politécnica de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital da UPB |
| oai_identifier_str | oai:bibliotecadigital.unipb.pt:10198/4421 |
| organization_str_mv | urn:organizationAcronym:unipb |
| person_str_mv | Rodrigues, Pedro Miguel Teixeira, João Paulo Teixeira, João Paulo https://www.ciencia-id.pt/4F15-B322-59B4 4F15-B322-59B4 http://orcid.org/0000-0002-6679-5702 0000-0002-6679-5702 |
| publishDate | 2010 |
| repo_facet_str | urn:repositoryAcronym:ipb{{{_:::_}}}Biblioteca Digital da UPB |
| reponame_str | Biblioteca Digital da UPB |
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| spelling | engporThe study of Artificial Neural Networks (ANN) has proved to be fascinating over the years and the development of these networks has grown strongly in recent years. The neural networks have come to be increasingly convincing methods for solving complex problems, through artificial intelligence. In particular this work focused on development of an artificial neural network for identifying diseases: Parkinson's, Huntington's and Amyotrophic Lateral Sclerosis, based on signals from the Electroencephalogram (EEG). The phases of the project were developed through a number of operations implemented in Matlab. The Fourier transform was seen as the main technique of signal processing, in order to analyze and diagnose diseases in the study. The work consisted in the first stage process the EEG signals to serve as an entry into the ANN in order to reveal a distinctive feature in the different diseases studied, and then, create a model capable to distinguish the diseases. For this purpose 4 methodologies were used with different processing of the EEG signal. The 4 methodologies are compared in this paper.application/pdfporClassification of electroencephalogram signals using artificial neural networksRodrigues, Pedro MiguelPersonalTeixeira, João PauloDSpacehttp://dspace.org/items/33f4af65-7ddf-46f0-8b44-a7470a8ba2bfDSpacehttp://dspace.org/items/33f4af65-7ddf-46f0-8b44-a7470a8ba2bfTeixeiraJoão PauloCiência IDhttps://www.ciencia-id.pt4F15-B322-59B4ORCIDhttp://orcid.org0000-0002-6679-5702Researcher IDhttps://www.researcherid.comN-6576-2013Scopus Author IDhttps://www.scopus.com57069567500HostingInstitutionOrganizationalBiblioteca Digital da UPBe-mailmailto:dspace@unipb.ptdspace@unipb.pt2011-05-20T09:33:29Z20102010-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/4421http://purl.org/coar/access_right/c_abf2open accessArtificial neural networksFFTEEGClassification257771 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paperhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://bibliotecadigital.unipb.pt/bitstreams/caa8456e-ee88-4e35-a628-a82287091338/downloadThe 3rd International Conference on BioMedical Engineering and Informatics (BMEI'10)Yantai – China |
| spellingShingle | Classification of electroencephalogram signals using artificial neural networks Rodrigues, Pedro Miguel Artificial neural networks FFT EEG Classification |
| status | SINGLETON |
| subject.fl_str_mv | Artificial neural networks FFT EEG Classification |
| title | Classification of electroencephalogram signals using artificial neural networks |
| title_full | Classification of electroencephalogram signals using artificial neural networks |
| title_fullStr | Classification of electroencephalogram signals using artificial neural networks |
| title_full_unstemmed | Classification of electroencephalogram signals using artificial neural networks |
| title_short | Classification of electroencephalogram signals using artificial neural networks |
| title_sort | Classification of electroencephalogram signals using artificial neural networks |
| topic | Artificial neural networks FFT EEG Classification |
| topic_facet | Artificial neural networks FFT EEG Classification |
| url | http://hdl.handle.net/10198/4421 |
| visible | 1 |
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