Ве молиме користете го овој идентификатор да го цитирате или поврзете овој запис: http://hdl.handle.net/20.500.12188/31216
DC FieldValueLanguage
dc.contributor.authorMladenovska, Teodoraen_US
dc.contributor.authorAckovska, Nevenaen_US
dc.contributor.authorKostoska, Magdalenaen_US
dc.contributor.authorKoteska, Bojanaen_US
dc.contributor.authorTrojachanec, Katarinaen_US
dc.contributor.authorMadevska Bogdanova, Anaen_US
dc.date.accessioned2024-08-30T06:01:32Z-
dc.date.available2024-08-30T06:01:32Z-
dc.date.issued2024-06-25-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/31216-
dc.description.abstractThe use of photoplethysmography (PPG) signals to predict the arterial blood pressure (ABP) waveform has gained popularity in recent years. Currently, there is a limited number of studies investigating this approach. This chapter elaborates a comparative analysis of two methodologies: a deep neural network approach and an encoder–decoder model for ABP waveform estimation with different window sizes expressed in seconds: 1s (175 signal points) and 4s (512 signal points). By estimating the systolic blood pressure (SBP) and diastolic blood pressure (DBP) scalars, this approach differs from conventional regression models that predict blood pressure through direct estimation; and it also enables another feature—evaluation of cardiovascular anomalies by analyzing the waveform patterns derived from the input PPG signal, which enables further medical analysis. The best obtained results are an R2 score of 0.76 for ABP, an MAE of 6.52 mmHg for DBP, using an encoder–decoder model on a sequence of 4s, and an MAE of 10.48 mmHg for SBP using GRU neural network on a sequence of 1s.en_US
dc.description.sponsorshipNATOen_US
dc.language.isoenen_US
dc.publisherSpringer, Chamen_US
dc.relationSmart Patch for Life Support Systems (SP4LIFE), G5825en_US
dc.titleComparison of Different Methods for Estimation of Arterial Blood Pressure Using PPG Signalsen_US
dc.typeProceeding articleen_US
dc.relation.conference7th EAI International Conference on Robotic Sensor Networks (ROSENET 2023)en_US
dc.identifier.doihttps://doi.org/10.1007/978-3-031-64495-5_13-
dc.identifier.eisbn978-3-031-64495-5-
item.fulltextNo Fulltext-
item.grantfulltextnone-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
Прикажи едноставен запис

Page view(s)

81
checked on 5.5.2025

Google ScholarTM

Проверете

Altmetric


Записите во DSpace се заштитени со авторски права, со сите права задржани, освен ако не е поинаку наведено.