Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/22821
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dc.contributor.authorHristina, Mitrovaen_US
dc.contributor.authorKoteska, Bojanaen_US
dc.contributor.authorMadevska Bogdanova, Anaen_US
dc.contributor.authorLehocki, Fedoren_US
dc.contributor.authorOndrusova, Beataen_US
dc.contributor.authorAckovska, Nevenaen_US
dc.date.accessioned2022-09-02T12:19:44Z-
dc.date.available2022-09-02T12:19:44Z-
dc.date.issued2021-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/22821-
dc.description.abstractHandling the mass casualty emergency situations can be improved by introducing a chest patch sensor that is able to deliver the main vital parameters: Heart Rate (HR), Respiration Rate (RR), SPO2 and Blood Pressure. The START triage procedure requires both HR and RR parameters almost instantly. In this paper we investigate the calculation of HR from a raw PPG signal, using appropriate functions from the Python HeartPy Tooklit, by comparing the calculated HR to the measured HR for the same patients, recorded at the same time as the PPG signal. By using several evaluation metrics, it was concluded that there is no significant difference between the measured and the calculated HR (MAE = 0,3, MSE=0,3, R2 =0,99, Pearson’s and the Spearman’s coefficient of correlation, 0.99). This result is the same whether raw or filtered PPG signal was used for the HR calculation.en_US
dc.subjectPhotoplethysmogram data · Signal processing · Heart rate analysis · Peak detection · Evaluation metricsen_US
dc.titleEvaluation of Python HeartPy Tooklit for Heart Rate extraction from PPGen_US
dc.typeProceedingsen_US
dc.relation.conferenceICT Innovations Conference 2021en_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
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
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