Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/22821
Title: | Evaluation of Python HeartPy Tooklit for Heart Rate extraction from PPG | Authors: | Hristina, Mitrova Koteska, Bojana Madevska Bogdanova, Ana Lehocki, Fedor Ondrusova, Beata Ackovska, Nevena |
Keywords: | Photoplethysmogram data · Signal processing · Heart rate analysis · Peak detection · Evaluation metrics | Issue Date: | 2021 | Conference: | ICT Innovations Conference 2021 | Abstract: | Handling 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. | URI: | http://hdl.handle.net/20.500.12188/22821 |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
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