DC FieldValueLanguage
dc.contributor.authorShi, Kilin-
dc.contributor.authorSchellenberger, Sven-
dc.contributor.authorWeber, Leon-
dc.contributor.authorWiedemann, Jan Philipp-
dc.contributor.authorMichler, Fabian-
dc.contributor.authorSteigleder, Tobias-
dc.contributor.authorMalessa, Anke-
dc.contributor.authorLurz, Fabian-
dc.contributor.authorOstgathe, Christoph-
dc.contributor.authorWeigel, Robert-
dc.contributor.authorKölpin, Alexander-
dc.date.accessioned2020-04-01T19:04:10Z-
dc.date.available2020-04-01T19:04:10Z-
dc.date.issued2019-07-
dc.identifier.citationAnnual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS: 8857863 (2019-07)de_DE
dc.identifier.isbn978-153861311-5de_DE
dc.identifier.issn1557-170Xde_DE
dc.identifier.urihttp://hdl.handle.net/11420/5592-
dc.description.abstractSounds caused by the action of the heart reflect both its health as well as deficiencies and are examined by physicians since antiquity. Pathologies of the valves, e.g. insufficiencies and stenosis, cardiac effusion, arrhythmia, inflammation of the surrounding tissue and other diagnosis can be reached by experienced physicians. However, practice is needed to assess the findings correctly. Furthermore, stethoscopes do not allow for long-term monitoring of a patient. Recently, radar technology has shown the ability to perform continuous touchless and thereby burden-free heart sound measurements. In order to perform automated classification of the signals, the first and most important step is to segment the heart sounds into their physiological phases. This paper examines the use of different Long Short-Term Memory (LSTM) architectures for this purpose based on a large dataset of radar-recorded heart sounds gathered from 30 different test persons in a clinical study. The best-performing network, a bidirectional LSTM, achieves a sample-wise accuracy of 93.4 % and a F1 score for the first heart sound of 95.8 %.en
dc.language.isoende_DE
dc.titleSegmentation of Radar-Recorded Heart Sound Signals Using Bidirectional LSTM Networksde_DE
dc.typeinProceedingsde_DE
dc.type.dinicontributionToPeriodical-
dcterms.DCMITypeText-
tuhh.abstract.englishSounds caused by the action of the heart reflect both its health as well as deficiencies and are examined by physicians since antiquity. Pathologies of the valves, e.g. insufficiencies and stenosis, cardiac effusion, arrhythmia, inflammation of the surrounding tissue and other diagnosis can be reached by experienced physicians. However, practice is needed to assess the findings correctly. Furthermore, stethoscopes do not allow for long-term monitoring of a patient. Recently, radar technology has shown the ability to perform continuous touchless and thereby burden-free heart sound measurements. In order to perform automated classification of the signals, the first and most important step is to segment the heart sounds into their physiological phases. This paper examines the use of different Long Short-Term Memory (LSTM) architectures for this purpose based on a large dataset of radar-recorded heart sounds gathered from 30 different test persons in a clinical study. The best-performing network, a bidirectional LSTM, achieves a sample-wise accuracy of 93.4 % and a F1 score for the first heart sound of 95.8 %.de_DE
tuhh.publisher.doi10.1109/EMBC.2019.8857863-
tuhh.type.opusInProceedings (Aufsatz / Paper einer Konferenz etc.)-
dc.type.drivercontributionToPeriodical-
dc.type.casraiConference Paper-
tuhh.container.startpage6677de_DE
tuhh.container.endpage6680de_DE
dc.relation.conferenceAnnual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2019)de_DE
tuhh.container.articlenumber8857863de_DE
item.creatorGNDShi, Kilin-
item.creatorGNDSchellenberger, Sven-
item.creatorGNDWeber, Leon-
item.creatorGNDWiedemann, Jan Philipp-
item.creatorGNDMichler, Fabian-
item.creatorGNDSteigleder, Tobias-
item.creatorGNDMalessa, Anke-
item.creatorGNDLurz, Fabian-
item.creatorGNDOstgathe, Christoph-
item.creatorGNDWeigel, Robert-
item.creatorGNDKölpin, Alexander-
item.languageiso639-1en-
item.fulltextNo Fulltext-
item.cerifentitytypePublications-
item.openairetypeinProceedings-
item.openairecristypehttp://purl.org/coar/resource_type/c_5794-
item.grantfulltextnone-
item.creatorOrcidShi, Kilin-
item.creatorOrcidSchellenberger, Sven-
item.creatorOrcidWeber, Leon-
item.creatorOrcidWiedemann, Jan Philipp-
item.creatorOrcidMichler, Fabian-
item.creatorOrcidSteigleder, Tobias-
item.creatorOrcidMalessa, Anke-
item.creatorOrcidLurz, Fabian-
item.creatorOrcidOstgathe, Christoph-
item.creatorOrcidWeigel, Robert-
item.creatorOrcidKölpin, Alexander-
crisitem.author.deptHochfrequenztechnik E-3-
crisitem.author.deptHochfrequenztechnik E-3-
crisitem.author.deptHochfrequenztechnik E-3-
crisitem.author.orcid0000-0001-9825-3927-
crisitem.author.orcid0000-0001-6884-6051-
crisitem.author.orcid0000-0003-1689-0960-
crisitem.author.orcid0000-0003-4948-9655-
crisitem.author.orcid0000-0003-4449-5036-
crisitem.author.orcid0000-0002-3131-1800-
crisitem.author.orcid0000-0002-9071-5661-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
crisitem.author.parentorgStudiendekanat Elektrotechnik, Informatik und Mathematik-
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