Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/33588
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dc.contributor.authorRocha, Bruno Machadopt_PT
dc.contributor.authorPessoa, Diogopt_PT
dc.contributor.authorCheimariotis, Grigorios-Arispt_PT
dc.contributor.authorKaimakamis, Evangelospt_PT
dc.contributor.authorKotoulas, Serafeim-Chrysovalantispt_PT
dc.contributor.authorTzimou, Myrtopt_PT
dc.contributor.authorMaglaveras, Nicospt_PT
dc.contributor.authorMarques, Aldapt_PT
dc.contributor.authorCarvalho, Paulo dept_PT
dc.contributor.authorPaiva, Rui Pedropt_PT
dc.date.accessioned2022-03-30T13:14:12Z-
dc.date.available2022-03-30T13:14:12Z-
dc.date.issued2021-
dc.identifier.issn2375-7477pt_PT
dc.identifier.urihttp://hdl.handle.net/10773/33588-
dc.description.abstractMechanically ventilated patients typically exhibit abnormal respiratory sounds. Squawks are short inspiratory adventitious sounds that may occur in patients with pneumonia, such as COVID-19 patients. In this work we devised a method for squawk detection in mechanically ventilated patients by developing algorithms for respiratory cycle estimation, squawk candidate identification, feature extraction, and clustering. The best classifier reached an F1 of 0.48 at the sound file level and an F1 of 0.66 at the recording session level. These preliminary results are promising, as they were obtained in noisy environments. This method will give health professionals a new feature to assess the potential deterioration of critically ill patients.pt_PT
dc.language.isoengpt_PT
dc.publisherIEEEpt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/POR_CENTRO/SFRH%2FBD%2F135686%2F2018/PTpt_PT
dc.relationDFA/BD/4927/2020pt_PT
dc.relationinfo:eu-repo/grantAgreement/EC/H2020/825572/EUpt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FAI%2F0113%2F2020/PTpt_PT
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04501%2F2020/PTpt_PT
dc.relationPOCI-01-0145-FEDER-007628pt_PT
dc.rightsopenAccesspt_PT
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectRespiratory Soundspt_PT
dc.subjectAudio Signal Processingpt_PT
dc.subjectIntensive Carept_PT
dc.titleDetection of squawks in respiratory sounds of mechanically ventilated COVID-19 patientspt_PT
dc.typearticlept_PT
dc.description.versionpublishedpt_PT
dc.peerreviewedyespt_PT
degois.publication.firstPage512pt_PT
degois.publication.lastPage516pt_PT
degois.publication.titleProceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)pt_PT
degois.publication.volume2021pt_PT
dc.identifier.doi10.1109/EMBC46164.2021.9630734pt_PT
dc.identifier.essn2694-0604pt_PT
Appears in Collections:IBIMED - Artigos
ESSUA - Artigos
Lab3R - Artigos

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