Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/11428
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dc.contributor.authorSalman, Hüseyin Enes-
dc.contributor.authorAl-Ruweidi, Mahmoud Khatib A.A.-
dc.contributor.authorOuakad, Hassen M.-
dc.contributor.authorYalçın, Hüseyin Çağatay-
dc.date.accessioned2024-04-06T08:11:50Z-
dc.date.available2024-04-06T08:11:50Z-
dc.date.issued2022-
dc.identifier.citationSalman, H. E., Al‐Ruweidi, M. K. A., Ouakad, H. M., & Yalcin, H. C. (2022). Minimally Invasive and Non‐Invasive Sensor Technologies for Predicting Heart Failure: An Overview. Predicting Heart Failure: Invasive, Non‐Invasive, Machine Learning and Artificial Intelligence Based Methods, 109-138.-
dc.identifier.isbn9781119813040-
dc.identifier.urihttps://doi.org/10.1002/9781119813040.ch5-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/11428-
dc.description.abstractThis chapter explains the non-invasive and minimally invasive sensor technologies and techniques employed for heart failure (HF) diagnosis. It summarizes landmark studies and clinical trials which prove the potential of non-invasive monitoring of HF patients. The methods for identifying worsening HF can be listed as body weight measurements, electrocardiography (ECG), bioimpedance monitoring, activity tracking, implanted pressure sensors, lung ultrasound monitoring, measurements with sound and Doppler sensors, seismocardiography, ballistocardiography, photoplethysmography, and measurement of natriuretic peptides levels in circulating blood. It is necessary to elucidate the effect of remote monitoring modalities for HF prediction on large-scale randomized control trials. A relative increase in thoracic bioimpedance provides better prediction of HF-related congestion. ECG is among the under-investigated techniques for HF remote monitoring. The positive results of clinical trials with large numbers of patients show the high potential of non-invasive sensors for diagnosing HF.en_US
dc.description.sponsorshipThis study was funded by Qatar National Research Fund (QNRF), National Priority Research Program (NPRP13S-0108-200024) and Qatar University International Research Collaboration Co-Fund (IRCC) program (IRCC-2020-002). Dr. Ouakad is grateful for the support of the Research Grant provided by the Deanship of Research at Sultan Qaboos University (SQU) through grant number CL/SQU-QU/ENG/20/01.en_US
dc.language.isoenen_US
dc.publisherJohn Wiley & Sons Ltd.en_US
dc.relation.ispartofPredicting Heart Failure: Invasive, Non‐Invasive,Machine Learning and Artificial Intelligence Based Methodsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleMinimally Invasive and Non-Invasive Sensor Technologies for Predicting Heart Failure An Overviewen_US
dc.typeBook Parten_US
dc.departmentTOBB ETU Mechanical Engineeringen_US
dc.identifier.startpage109en_US
dc.identifier.endpage138en_US
dc.authorid0000-0001-7572-9902-
dc.institutionauthorSalman, Hüseyin Enes-
dc.identifier.doi10.1002/9781119813040.ch5-
dc.relation.publicationcategoryKitap Bölümü - Uluslararasıen_US
item.grantfulltextnone-
item.openairetypeBook Part-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.languageiso639-1en-
crisitem.author.dept02.7. Department of Mechanical Engineering-
Appears in Collections:Makine Mühendisliği Bölümü / Department of Mechanical Engineering
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