Correlation Analysis Between Vital Signs Of Patients In Intensive Care Unit

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dc.contributor.authorOlcay, Firat Fuat
dc.contributor.authorDuru, Dilek Goksel
dc.date.accessioned2025-02-20T08:42:15Z
dc.date.available2025-02-20T08:42:15Z
dc.date.issued2024
dc.departmentTürk-Alman Üniversitesien_US
dc.description32nd IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2024 -- Tarsus Univ Campus, Mersin, TURKEYen_US
dc.description.abstractArtificial intelligence (AI) and machine learning (ML) techniques have become very important in healthcare and health data processing. The proliferation of the internet and technological advancements has led to the need to use various platforms to fulfill daily routines. As a result, there is an increase in personalized user experiences, especially in healthcare monitoring through non-invasive vital signs analysis. The widespread use of artificial intelligence and machine learning technologies in the field has increased the accuracy and accessibility of the analysis of health monitoring systems, especially by leveraging comprehensive databases such as the MIMIC-III Clinical Database (v1.4), thus eliminating the need for measurements or additional sensors for data collection. In this study, the MIMIC-III waveform database was used to examine the relationship between patients' non-invasive vital signs and the intensive care units in which they were admitted. The correlation information obtained started with the comparison of raw versions of the data and continued with mathematical transformations such as Fourier transforms to detect hidden patterns and search for significant relationships between vital signs in many dimensions, and the results are reported in this study. This correlation can be used to optimize data processing and hyperparameter settings before using machine learning and deep learning techniques in vital signs analysis.
dc.description.sponsorshipIEEE,IEEE Turkey,Koluman & Berdan,Loodos,Figes,Turkcell,Yildirim Elect
dc.identifier.doi10.1109/SIU61531.2024.10601026
dc.identifier.isbn979-8-3503-8897-8
dc.identifier.isbn979-8-3503-8896-1
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-85200915581
dc.identifier.urihttps://doi.org/10.1109/SIU61531.2024.10601026
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1603
dc.identifier.wosWOS:001297894700242
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isotr
dc.publisherIEEE
dc.relation.ispartof32nd Ieee Signal Processing and Communications Applications Conference, Siu 2024
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250220
dc.subjectnon-invasive vital signsen_US
dc.subjectMIMIC-III clinical databaseen_US
dc.subjectdata processingen_US
dc.subjectSpO2en_US
dc.subjectpulseen_US
dc.subjectheart rateen_US
dc.subjecthealthen_US
dc.subjecttime seriesen_US
dc.titleCorrelation Analysis Between Vital Signs Of Patients In Intensive Care Unit
dc.typeConference Object

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