Publicação
Sleep Stage Classification: A Deep Learning Approach
| Resumo: | Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity. |
|---|---|
| Autores principais: | Gharbali, Ali Abdollahi |
| Assunto: | Sleep Stage Classification Deep Learning Convolutional Neural Networks Transfer Learning Wavelet Adaptive Filtering |
| Ano: | 2018 |
| País: | Portugal |
| Tipo de documento: | tese de doutoramento |
| Tipo de acesso: | acesso aberto |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| _version_ | 1868415486963744768 |
|---|---|
| author | Gharbali, Ali Abdollahi |
| author_facet | Gharbali, Ali Abdollahi |
| author_role | author |
| contributor_name_str_mv | Fonseca, José RUN |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Gharbali, Ali Abdollahi\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Fonseca, José RUN |
| datacite.creators.creator.creatorName.fl_str_mv | Gharbali, Ali Abdollahi |
| datacite.date.Accepted.fl_str_mv | 2018-11-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2019-01-08T13:25:03Z |
| datacite.date.embargoed.fl_str_mv | 2019-01-08T13:25:03Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_abf2 |
| datacite.subjects.subject.fl_str_mv | Sleep Stage Classification Deep Learning Convolutional Neural Networks Transfer Learning Wavelet Adaptive Filtering |
| datacite.titles.title.fl_str_mv | Sleep Stage Classification: A Deep Learning Approach |
| dc.contributor.none.fl_str_mv | Fonseca, José RUN |
| dc.creator.none.fl_str_mv | Gharbali, Ali Abdollahi |
| dc.date.Accepted.fl_str_mv | 2018-11-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2019-01-08T13:25:03Z |
| dc.date.embargoed.fl_str_mv | 2019-01-08T13:25:03Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10362/56821 |
| 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 | Sleep Stage Classification Deep Learning Convolutional Neural Networks Transfer Learning Wavelet Adaptive Filtering |
| dc.title.fl_str_mv | Sleep Stage Classification: A Deep Learning Approach |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_db06 |
| description | Sleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity. |
| dirty | 0 |
| eu_rights_str_mv | openAccess |
| format | doctoralThesis |
| fulltext.url.fl_str_mv | https://run.unl.pt/bitstreams/b2a00114-8f75-4e23-8d98-ad52b2e4de5e/download |
| id | run_ede9ad4e446b45e3a83cd42e2145fa2a |
| identifier.url.fl_str_mv | http://hdl.handle.net/10362/56821 |
| instacron_str | unl |
| institution | Universidade Nova de Lisboa |
| instname_str | Universidade Nova de Lisboa |
| language | eng |
| network_acronym_str | run |
| network_name_str | Repositório Institucional da UNL |
| oai_identifier_str | oai:run.unl.pt:10362/56821 |
| organization_str_mv | urn:organizationAcronym:unl |
| person_str_mv | Gharbali, Ali Abdollahi |
| publishDate | 2018 |
| reponame_str | Repositório Institucional da UNL |
| repository_id_str | urn:repositoryAcronym:run |
| service_str_mv | urn:repositoryAcronym:run |
| spelling | engpt_PTSleep occupies significant part of human life. The diagnoses of sleep related disorders are of great importance. To record specific physical and electrical activities of the brain and body, a multi-parameter test, called polysomnography (PSG), is normally used. The visual process of sleep stage classification is time consuming, subjective and costly. To improve the accuracy and efficiency of the sleep stage classification, automatic classification algorithms were developed. In this research work, we focused on pre-processing (filtering boundaries and de-noising algorithms) and classification steps of automatic sleep stage classification. The main motivation for this work was to develop a pre-processing and classification framework to clean the input EEG signal without manipulating the original data thus enhancing the learning stage of deep learning classifiers. For pre-processing EEG signals, a lossless adaptive artefact removal method was proposed. Rather than other works that used artificial noise, we used real EEG data contaminated with EOG and EMG for evaluating the proposed method. The proposed adaptive algorithm led to a significant enhancement in the overall classification accuracy. In the classification area, we evaluated the performance of the most common sleep stage classifiers using a comprehensive set of features extracted from PSG signals. Considering the challenges and limitations of conventional methods, we proposed two deep learning-based methods for classification of sleep stages based on Stacked Sparse AutoEncoder (SSAE) and Convolutional Neural Network (CNN). The proposed methods performed more efficiently by eliminating the need for conventional feature selection and feature extraction steps respectively. Moreover, although our systems were trained with lower number of samples compared to the similar studies, they were able to achieve state of art accuracy and higher overall sensitivity.application/pdfpt_PTSleep Stage Classification: A Deep Learning ApproachGharbali, Ali AbdollahiFonseca, JoséHostingInstitutionOrganizationalRUNe-mailmailto:run@unl.ptrun@unl.ptURNurn:tid:1016150512019-01-08T13:25:03Z2018-1120182018-11-01T00:00:00ZHandlehttp://hdl.handle.net/10362/56821http://purl.org/coar/access_right/c_abf2open accessSleep Stage ClassificationDeep LearningConvolutional Neural NetworksTransfer LearningWaveletAdaptive Filtering3977041 bytesliteraturehttp://purl.org/coar/resource_type/c_db06doctoral thesishttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://run.unl.pt/bitstreams/b2a00114-8f75-4e23-8d98-ad52b2e4de5e/download |
| spellingShingle | Sleep Stage Classification: A Deep Learning Approach Gharbali, Ali Abdollahi Sleep Stage Classification Deep Learning Convolutional Neural Networks Transfer Learning Wavelet Adaptive Filtering |
| status | SINGLETON |
| subject.fl_str_mv | Sleep Stage Classification Deep Learning Convolutional Neural Networks Transfer Learning Wavelet Adaptive Filtering |
| title | Sleep Stage Classification: A Deep Learning Approach |
| title_full | Sleep Stage Classification: A Deep Learning Approach |
| title_fullStr | Sleep Stage Classification: A Deep Learning Approach |
| title_full_unstemmed | Sleep Stage Classification: A Deep Learning Approach |
| title_short | Sleep Stage Classification: A Deep Learning Approach |
| title_sort | Sleep Stage Classification: A Deep Learning Approach |
| topic | Sleep Stage Classification Deep Learning Convolutional Neural Networks Transfer Learning Wavelet Adaptive Filtering |
| topic_facet | Sleep Stage Classification Deep Learning Convolutional Neural Networks Transfer Learning Wavelet Adaptive Filtering |
| url | http://hdl.handle.net/10362/56821 |
| visible | 1 |