Publicação
Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models
| Resumo: | Objective: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. Methods: We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. Results: Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. Significance: This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG. |
|---|---|
| Autores principais: | Viana, Pedro |
| Outros Autores: | Pal Attia, Tal; Nasseri, Mona; Duun‐Henriksen, Jonas; Biondi, Andrea; Winston, Joel S.; Martins, Isabel Pavão; Nurse, Ewan S.; Dümpelmann, Matthias; Schulze‐Bonhage, Andreas; Freestone, Dean R.; Kjaer, Troels W.; Richardson, Mark P.; Brinkmann, Benjamin H. |
| Assunto: | deep learning Epilepsy Mobile health Seizure forecasting Seizure prediction Subcutaneous EEG |
| Ano: | 2022 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório da Universidade de Lisboa |
| _version_ | 1866810025102540800 |
|---|---|
| author | Viana, Pedro |
| author2 | Pal Attia, Tal Nasseri, Mona Duun‐Henriksen, Jonas Biondi, Andrea Winston, Joel S. Martins, Isabel Pavão Nurse, Ewan S. Dümpelmann, Matthias Schulze‐Bonhage, Andreas Freestone, Dean R. Kjaer, Troels W. Richardson, Mark P. Brinkmann, Benjamin H. |
| author2_role | author author author author author author author author author author author author author |
| author_facet | Viana, Pedro Pal Attia, Tal Nasseri, Mona Duun‐Henriksen, Jonas Biondi, Andrea Winston, Joel S. Martins, Isabel Pavão Nurse, Ewan S. Dümpelmann, Matthias Schulze‐Bonhage, Andreas Freestone, Dean R. Kjaer, Troels W. Richardson, Mark P. Brinkmann, Benjamin H. |
| author_role | author |
| contributor_name_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Viana, Pedro\",\"Person.identifier.orcid\":\"0000-0003-0861-8705\"},{\"Person.name\":\"Pal Attia, Tal\"},{\"Person.name\":\"Nasseri, Mona\"},{\"Person.name\":\"Duun‐Henriksen, Jonas\"},{\"Person.name\":\"Biondi, Andrea\"},{\"Person.name\":\"Winston, Joel S.\"},{\"Person.name\":\"Martins, Isabel Pavão\",\"Person.identifier.orcid\":\"0000-0002-9611-7400\"},{\"Person.name\":\"Nurse, Ewan S.\"},{\"Person.name\":\"Dümpelmann, Matthias\"},{\"Person.name\":\"Schulze‐Bonhage, Andreas\"},{\"Person.name\":\"Freestone, Dean R.\"},{\"Person.name\":\"Kjaer, Troels W.\"},{\"Person.name\":\"Richardson, Mark P.\"},{\"Person.name\":\"Brinkmann, Benjamin H.\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| datacite.creators.creator.creatorName.fl_str_mv | Viana, Pedro Pal Attia, Tal Nasseri, Mona Duun‐Henriksen, Jonas Biondi, Andrea Winston, Joel S. Martins, Isabel Pavão Nurse, Ewan S. Dümpelmann, Matthias Schulze‐Bonhage, Andreas Freestone, Dean R. Kjaer, Troels W. Richardson, Mark P. Brinkmann, Benjamin H. |
| datacite.date.Accepted.fl_str_mv | 2022-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2022-04-21T15:58:00Z |
| datacite.date.embargoed.fl_str_mv | 2022-04-21T15:58:00Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | deep learning Epilepsy Mobile health Seizure forecasting Seizure prediction Subcutaneous EEG |
| datacite.titles.title.fl_str_mv | Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models |
| dc.contributor.none.fl_str_mv | Repositório Científico de Acesso Aberto da ULisboa |
| dc.creator.none.fl_str_mv | Viana, Pedro Pal Attia, Tal Nasseri, Mona Duun‐Henriksen, Jonas Biondi, Andrea Winston, Joel S. Martins, Isabel Pavão Nurse, Ewan S. Dümpelmann, Matthias Schulze‐Bonhage, Andreas Freestone, Dean R. Kjaer, Troels W. Richardson, Mark P. Brinkmann, Benjamin H. |
| dc.date.Accepted.fl_str_mv | 2022-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2022-04-21T15:58:00Z |
| dc.date.embargoed.fl_str_mv | 2022-04-21T15:58:00Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | http://hdl.handle.net/10451/52525 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | Wiley |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | deep learning Epilepsy Mobile health Seizure forecasting Seizure prediction Subcutaneous EEG |
| dc.title.fl_str_mv | Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | Objective: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. Methods: We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. Results: Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. Significance: This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG. |
| dirty | 0 |
| eu_rights_str_mv | restrictedAccess |
| format | article |
| fulltext.url.fl_str_mv | https://repositorio.ulisboa.pt/bitstreams/13ffb2d9-0b6b-458d-aa24-99ce3d8d351a/download |
| funding.funder.alternateName_str_mv | EC |
| funding.funder.identifier_str_mv | http://doi.org/10.13039/501100008530 |
| funding.funder.name_str_mv | European Commission |
| funding.name_str_mv | H2020 |
| id | ul_62b71fccd7400a0bcdc4295caf7755d2 |
| identifier.url.fl_str_mv | http://hdl.handle.net/10451/52525 |
| instacron_str | ul |
| institution | Universidade de Lisboa |
| instname_str | Universidade de Lisboa |
| language | eng |
| network_acronym_str | ul |
| network_name_str | Repositório da Universidade de Lisboa |
| oai_identifier_str | oai:repositorio.ulisboa.pt:10451/52525 |
| organization_str_mv | urn:organizationAcronym:ul |
| person_str_mv | Viana, Pedro Viana, Pedro http://orcid.org/0000-0003-0861-8705 0000-0003-0861-8705 Pal Attia, Tal Nasseri, Mona Duun‐Henriksen, Jonas Biondi, Andrea Winston, Joel S. Martins, Isabel Pavão Martins, Isabel Pavão https://www.ciencia-id.pt/4D1D-4040-BE76 4D1D-4040-BE76 http://orcid.org/0000-0002-9611-7400 0000-0002-9611-7400 Nurse, Ewan S. Dümpelmann, Matthias Schulze‐Bonhage, Andreas Freestone, Dean R. Kjaer, Troels W. Richardson, Mark P. Brinkmann, Benjamin H. |
| publishDate | 2022 |
| publisher.none.fl_str_mv | Wiley |
| reponame_str | Repositório da Universidade de Lisboa |
| repository_id_str | urn:repositoryAcronym:ul |
| service_str_mv | urn:repositoryAcronym:ul |
| spelling | engWileypt_PTObjective: One of the most disabling aspects of living with chronic epilepsy is the unpredictability of seizures. Cumulative research in the past decades has advanced our understanding of the dynamics of seizure risk. Technological advances have recently made it possible to record pertinent biological signals, including electroencephalogram (EEG), continuously. We aimed to assess whether patient-specific seizure forecasting is possible using remote, minimally invasive ultra-long-term subcutaneous EEG. Methods: We analyzed a two-center cohort of ultra-long-term subcutaneous EEG recordings, including six patients with drug-resistant focal epilepsy monitored for 46-230 days with median 18 h/day of recorded data, totaling >11 000 h of EEG. Total electrographic seizures identified by visual review ranged from 12 to 36 per patient. Three candidate subject-specific long short-term memory network deep learning classifiers were trained offline and pseudoprospectively on preictal (1 h before) and interictal (>1 day from seizures) EEG segments. Performance was assessed relative to a random predictor. Periodicity of the final forecasts was also investigated with autocorrelation. Results: Depending on each architecture, significant forecasting performance was achieved in three to five of six patients, with overall mean area under the receiver operating characteristic curve of .65-.74. Significant forecasts showed sensitivity ranging from 64% to 80% and time in warning from 10.9% to 44.4%. Overall, the output of the forecasts closely followed patient-specific circadian patterns of seizure occurrence. Significance: This study demonstrates proof-of-principle for the possibility of subject-specific seizure forecasting using a minimally invasive subcutaneous EEG device capable of ultra-long-term at-home recordings. These results are encouraging for the development of a prospective seizure forecasting trial with minimally invasive EEG.application/pdfpt_PTSeizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient modelsPersonalViana, PedroDSpacehttp://dspace.org/items/e22897a7-0bb2-4e48-8b22-d70ddea4c03fDSpacehttp://dspace.org/items/e22897a7-0bb2-4e48-8b22-d70ddea4c03fVianaPedroORCIDhttp://orcid.org0000-0003-0861-8705Pal Attia, TalNasseri, MonaDuun‐Henriksen, JonasBiondi, AndreaWinston, Joel S.PersonalMartins, Isabel PavãoDSpacehttp://dspace.org/items/d134f8f9-be2a-4990-b8ec-3159ff3c51f2DSpacehttp://dspace.org/items/d134f8f9-be2a-4990-b8ec-3159ff3c51f2Pavão MartinsIsabelCiência IDhttps://www.ciencia-id.pt4D1D-4040-BE76ORCIDhttp://orcid.org0000-0002-9611-7400Scopus Author IDhttps://www.scopus.com7103152782Nurse, Ewan S.Dümpelmann, MatthiasSchulze‐Bonhage, AndreasFreestone, Dean R.Kjaer, Troels W.Richardson, Mark P.Brinkmann, Benjamin H.HostingInstitutionOrganizationalRepositório Científico de Acesso Aberto da ULisboae-mailmailto:repositorio@reitoria.ulisboa.ptrepositorio@reitoria.ulisboa.ptISSNIsPartOf0013-9580DOIIsPartOf10.1111/epi.172522022-04-21T15:58:00Z20222022-01-01T00:00:00ZHandlehttp://hdl.handle.net/10451/52525http://purl.org/coar/access_right/c_16ecrestricted accessdeep learningEpilepsyMobile healthSeizure forecastingSeizure predictionSubcutaneous EEG985242 bytesEuropean CommissionRemote Assessment of Disease and Relapse in Central Nervous System DisordersH2020Crossref Funder IDhttp://doi.org/10.13039/501100008530literaturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://repositorio.ulisboa.pt/bitstreams/13ffb2d9-0b6b-458d-aa24-99ce3d8d351a/downloadEpilepsia |
| spellingShingle | Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models Viana, Pedro deep learning Epilepsy Mobile health Seizure forecasting Seizure prediction Subcutaneous EEG |
| status | SINGLETON |
| subject.fl_str_mv | deep learning Epilepsy Mobile health Seizure forecasting Seizure prediction Subcutaneous EEG |
| title | Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models |
| title_full | Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models |
| title_fullStr | Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models |
| title_full_unstemmed | Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models |
| title_short | Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models |
| title_sort | Seizure forecasting using minimally invasive, ultra‐long‐term subcutaneous electroencephalography: individualized intrapatient models |
| topic | deep learning Epilepsy Mobile health Seizure forecasting Seizure prediction Subcutaneous EEG |
| topic_facet | deep learning Epilepsy Mobile health Seizure forecasting Seizure prediction Subcutaneous EEG |
| url | http://hdl.handle.net/10451/52525 |
| visible | 1 |