PPG signal characterization using windkessel model

The cardiovascular system is one of the most important systems in the life process of humans and animals. At present, cardiovascular diseases have become a common problem affecting human life and health. Therefore, safe and reliable detection of non-invasive vital signs has received more and more at...

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Main Author: Zheng, Hongzhi
Other Authors: Saman S Abeysekera
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2020
Subjects:
Online Access:https://hdl.handle.net/10356/141418
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1414182023-07-04T16:16:03Z PPG signal characterization using windkessel model Zheng, Hongzhi Saman S Abeysekera School of Electrical and Electronic Engineering Esabeysekera@ntu.edu.sg Engineering::Electrical and electronic engineering The cardiovascular system is one of the most important systems in the life process of humans and animals. At present, cardiovascular diseases have become a common problem affecting human life and health. Therefore, safe and reliable detection of non-invasive vital signs has received more and more attention recently. A common method is to use pulse oximeter (PPG) signals collected through wearable sensors such as smart watches. Through the establishment of a model to study the characteristics of PPG signal to analyze the subject's cardiovascular health, it is possible to prevent diseases and assist in the purpose of detecting the disease. In this thesis, the most widely used dual elastic cavity model is used to characterize and study the PPG signal. The corresponding electrical network model is established according to the cardiovascular system characteristics, and four parameters C_1, C_2, R, and L are determined to investigate the cardiovascular properties. The PPG signal is used to predict the parameter values through curve fitting and error analysis has been performed using the parameters. Master of Science (Signal Processing) 2020-06-08T06:34:56Z 2020-06-08T06:34:56Z 2020 Thesis-Master by Coursework https://hdl.handle.net/10356/141418 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Electrical and electronic engineering
spellingShingle Engineering::Electrical and electronic engineering
Zheng, Hongzhi
PPG signal characterization using windkessel model
description The cardiovascular system is one of the most important systems in the life process of humans and animals. At present, cardiovascular diseases have become a common problem affecting human life and health. Therefore, safe and reliable detection of non-invasive vital signs has received more and more attention recently. A common method is to use pulse oximeter (PPG) signals collected through wearable sensors such as smart watches. Through the establishment of a model to study the characteristics of PPG signal to analyze the subject's cardiovascular health, it is possible to prevent diseases and assist in the purpose of detecting the disease. In this thesis, the most widely used dual elastic cavity model is used to characterize and study the PPG signal. The corresponding electrical network model is established according to the cardiovascular system characteristics, and four parameters C_1, C_2, R, and L are determined to investigate the cardiovascular properties. The PPG signal is used to predict the parameter values through curve fitting and error analysis has been performed using the parameters.
author2 Saman S Abeysekera
author_facet Saman S Abeysekera
Zheng, Hongzhi
format Thesis-Master by Coursework
author Zheng, Hongzhi
author_sort Zheng, Hongzhi
title PPG signal characterization using windkessel model
title_short PPG signal characterization using windkessel model
title_full PPG signal characterization using windkessel model
title_fullStr PPG signal characterization using windkessel model
title_full_unstemmed PPG signal characterization using windkessel model
title_sort ppg signal characterization using windkessel model
publisher Nanyang Technological University
publishDate 2020
url https://hdl.handle.net/10356/141418
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