Please use this identifier to cite or link to this item:
http://hdl.handle.net/20.500.12188/31216
Title: | Comparison of Different Methods for Estimation of Arterial Blood Pressure Using PPG Signals | Authors: | Mladenovska, Teodora Ackovska, Nevena Kostoska, Magdalena Koteska, Bojana Trojachanec, Katarina Madevska Bogdanova, Ana |
Issue Date: | 25-Jun-2024 | Publisher: | Springer, Cham | Project: | Smart Patch for Life Support Systems (SP4LIFE), G5825 | Conference: | 7th EAI International Conference on Robotic Sensor Networks (ROSENET 2023) | Abstract: | The 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. | URI: | http://hdl.handle.net/20.500.12188/31216 | DOI: | https://doi.org/10.1007/978-3-031-64495-5_13 |
Appears in Collections: | Faculty of Computer Science and Engineering: Conference papers |
Show full item record
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.