Publicação
Alzheimer’s disease recognition with artificial neural networks
| Resumo: | Alzheimer’s disease (AD) is the most common cause of dementia, and is well known for affect the memory loss and other intellectual disabilities. The Electroencephalogram (EEG) has been used as diagnosis tool for dementia over several decades. The main objective of this work was to develop an Artificial Neural Network (ANN) to classify EEG signals between AD patients and Control subjects. For this purpose two different methodologies and variations were used. The Short time Fourier transform (STFT) was applied to one of the methodologies and the Wavelet Transform (WT) was applied to the other methodology. The studied features of the EEG signals were the Relative Power in conventional EEG bands (delta, theta, alpha, beta and gamma) and their associated Spectral Ratios (r1, r2, r3 and r4). The best classification was performed by the ANN using the WT Biorthogonal 3.5 with AROC of 0.97, Sensitivity of 92.1%, Specificity of 90.8% and 91.5% of Accuracy. |
|---|---|
| Autores principais: | Rodrigues, Pedro Miguel |
| Outros Autores: | Teixeira, João Paulo |
| Assunto: | Artificial neural networks Wavelet transform Short time fourier transform Alzheimer’s disease Electroencephalogram |
| Ano: | 2013 |
| País: | Portugal |
| Tipo de documento: | capítulo de livro |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Instituto Politécnico de Bragança |
| Idioma: | inglês |
| Origem: | Biblioteca Digital do IPB |
| _version_ | 1867172974995439616 |
|---|---|
| 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 do IPB |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Rodrigues, Pedro Miguel\"},{\"Person.name\":\"Teixeira, João Paulo\",\"Person.identifier.orcid\":\"0000-0002-6679-5702\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Biblioteca Digital do IPB |
| datacite.creators.creator.creatorName.fl_str_mv | Rodrigues, Pedro Miguel Teixeira, João Paulo |
| datacite.date.Accepted.fl_str_mv | 2013-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2014-10-29T16:05:40Z |
| datacite.date.embargoed.fl_str_mv | 2014-10-29T16:05:40Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | Artificial neural networks Wavelet transform Short time fourier transform Alzheimer’s disease Electroencephalogram |
| datacite.titles.title.fl_str_mv | Alzheimer’s disease recognition with artificial neural networks |
| dc.contributor.none.fl_str_mv | Biblioteca Digital do IPB |
| dc.creator.none.fl_str_mv | Rodrigues, Pedro Miguel Teixeira, João Paulo |
| dc.date.Accepted.fl_str_mv | 2013-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2014-10-29T16:05:40Z |
| dc.date.embargoed.fl_str_mv | 2014-10-29T16:05:40Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10198/11186 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | IGI Global |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | Artificial neural networks Wavelet transform Short time fourier transform Alzheimer’s disease Electroencephalogram |
| dc.title.fl_str_mv | Alzheimer’s disease recognition with artificial neural networks |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_3248 |
| description | Alzheimer’s disease (AD) is the most common cause of dementia, and is well known for affect the memory loss and other intellectual disabilities. The Electroencephalogram (EEG) has been used as diagnosis tool for dementia over several decades. The main objective of this work was to develop an Artificial Neural Network (ANN) to classify EEG signals between AD patients and Control subjects. For this purpose two different methodologies and variations were used. The Short time Fourier transform (STFT) was applied to one of the methodologies and the Wavelet Transform (WT) was applied to the other methodology. The studied features of the EEG signals were the Relative Power in conventional EEG bands (delta, theta, alpha, beta and gamma) and their associated Spectral Ratios (r1, r2, r3 and r4). The best classification was performed by the ANN using the WT Biorthogonal 3.5 with AROC of 0.97, Sensitivity of 92.1%, Specificity of 90.8% and 91.5% of Accuracy. |
| dirty | 0 |
| eu_rights_str_mv | restrictedAccess |
| format | bookPart |
| fulltext.url.fl_str_mv | https://bibliotecadigital.ipb.pt/bitstreams/febd3ca7-7712-46b3-a745-51e2d02d8dbb/download |
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| identifier.url.fl_str_mv | http://hdl.handle.net/10198/11186 |
| instacron_str | ipb |
| institution | Instituto Politécnico de Bragança |
| instname_str | Instituto Politécnico de Bragança |
| language | eng |
| network_acronym_str | ipb |
| network_name_str | Biblioteca Digital do IPB |
| oai_identifier_str | oai:bibliotecadigital.ipb.pt:10198/11186 |
| organization_str_mv | urn:organizationAcronym:ipb |
| 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 | 2013 |
| publisher.none.fl_str_mv | IGI Global |
| reponame_str | Biblioteca Digital do IPB |
| repository_id_str | urn:repositoryAcronym:ipb |
| service_str_mv | urn:repositoryAcronym:ipb |
| spelling | engIGI GlobalporAlzheimer’s disease (AD) is the most common cause of dementia, and is well known for affect the memory loss and other intellectual disabilities. The Electroencephalogram (EEG) has been used as diagnosis tool for dementia over several decades. The main objective of this work was to develop an Artificial Neural Network (ANN) to classify EEG signals between AD patients and Control subjects. For this purpose two different methodologies and variations were used. The Short time Fourier transform (STFT) was applied to one of the methodologies and the Wavelet Transform (WT) was applied to the other methodology. The studied features of the EEG signals were the Relative Power in conventional EEG bands (delta, theta, alpha, beta and gamma) and their associated Spectral Ratios (r1, r2, r3 and r4). The best classification was performed by the ANN using the WT Biorthogonal 3.5 with AROC of 0.97, Sensitivity of 92.1%, Specificity of 90.8% and 91.5% of Accuracy.application/pdfporAlzheimer’s disease recognition with 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 do IPBe-mailmailto:dspace@ipb.ptdspace@ipb.ptISBNIsPartOf978-1-4666-3667-52014-10-29T16:05:40Z20132013-01-01T00:00:00ZHandlehttp://hdl.handle.net/10198/11186http://purl.org/coar/access_right/c_16ecrestricted accessArtificial neural networksWavelet transformShort time fourier transformAlzheimer’s diseaseElectroencephalogram4394028 bytesliteraturehttp://purl.org/coar/resource_type/c_3248book parthttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://bibliotecadigital.ipb.pt/bitstreams/febd3ca7-7712-46b3-a745-51e2d02d8dbb/downloadInformation Systemas and Technologies for Enhancing Health and Social Care102119USA |
| spellingShingle | Alzheimer’s disease recognition with artificial neural networks Rodrigues, Pedro Miguel Artificial neural networks Wavelet transform Short time fourier transform Alzheimer’s disease Electroencephalogram |
| status | SINGLETON |
| subject.fl_str_mv | Artificial neural networks Wavelet transform Short time fourier transform Alzheimer’s disease Electroencephalogram |
| title | Alzheimer’s disease recognition with artificial neural networks |
| title_full | Alzheimer’s disease recognition with artificial neural networks |
| title_fullStr | Alzheimer’s disease recognition with artificial neural networks |
| title_full_unstemmed | Alzheimer’s disease recognition with artificial neural networks |
| title_short | Alzheimer’s disease recognition with artificial neural networks |
| title_sort | Alzheimer’s disease recognition with artificial neural networks |
| topic | Artificial neural networks Wavelet transform Short time fourier transform Alzheimer’s disease Electroencephalogram |
| topic_facet | Artificial neural networks Wavelet transform Short time fourier transform Alzheimer’s disease Electroencephalogram |
| url | http://hdl.handle.net/10198/11186 |
| visible | 1 |