A dynamical model for generating synthetic ballistocardiogram signals based on Extended Kalman Filter (EKF)

In recent years, the ballistocardiogray technology receives many interests due to the development in both measurement methods and signal processing techniques. As a non-intrusive method for obtaining representation of the cardiovascular performance, it can be used as an effective as well as economic...

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Bibliographic Details
Main Author: Zhang, Qifan
Other Authors: Lin Zhiping
Format: Thesis
Language:English
Published: 2016
Subjects:
Online Access:http://hdl.handle.net/10356/68982
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author Zhang, Qifan
author2 Lin Zhiping
author_facet Lin Zhiping
Zhang, Qifan
author_sort Zhang, Qifan
collection NTU
description In recent years, the ballistocardiogray technology receives many interests due to the development in both measurement methods and signal processing techniques. As a non-intrusive method for obtaining representation of the cardiovascular performance, it can be used as an effective as well as economical tool for long-term home monitoring of cardiovascular diseases. In our study, the ballistocardiogram (BCG) data were obtained from fiber optic sensors which put in the seat mat of a chair, which has the characteristics of lighter weight and higher accuracy compared with popular-used methods, such as force plate and static charge-sensitive sensor.In this dissertation we proposed a BCG dynamical model in combination with Extended Kalman Filter (EKF) for BCG signals which is able to generate synthetic BCG signals as well as reduce nonlinear noises in real BCG signal. EKF algorithm is used in nonlinear denoising process to obtain BCG waveform which can approach standard BCG morphology. The feature points were extracted after the desired waveforms were obtained. Then the time and phase information, like Beat-to-Beat, IJ and JK time intervals within a typical BCG waveform were calculated. The proposed BCG dynamical model is composed of three coupled ordinary differential equations i.e. Gaussian kernel functions. The synthetic BCG signal which can illustrate beat-to-beat trajectory variation in BCG morphology was then outputted from the dynamical model. This model was evaluated by adding white and Gaussian noises artificially on several normal and real BCGs and synthetic BCGs, and the output SNR and morphology changes of the filter outputs was investigated. Results show that the dynamical model can be effectively used as a valid application in synthetic bio-signal generation and nonlinear system processing. In the near future, this novel model can be utilized to evaluate bio-signal processing algorithms and help extract clinical views from the real BCG signal.
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spelling ntu-10356/689822023-07-04T15:47:36Z A dynamical model for generating synthetic ballistocardiogram signals based on Extended Kalman Filter (EKF) Zhang, Qifan Lin Zhiping School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing In recent years, the ballistocardiogray technology receives many interests due to the development in both measurement methods and signal processing techniques. As a non-intrusive method for obtaining representation of the cardiovascular performance, it can be used as an effective as well as economical tool for long-term home monitoring of cardiovascular diseases. In our study, the ballistocardiogram (BCG) data were obtained from fiber optic sensors which put in the seat mat of a chair, which has the characteristics of lighter weight and higher accuracy compared with popular-used methods, such as force plate and static charge-sensitive sensor.In this dissertation we proposed a BCG dynamical model in combination with Extended Kalman Filter (EKF) for BCG signals which is able to generate synthetic BCG signals as well as reduce nonlinear noises in real BCG signal. EKF algorithm is used in nonlinear denoising process to obtain BCG waveform which can approach standard BCG morphology. The feature points were extracted after the desired waveforms were obtained. Then the time and phase information, like Beat-to-Beat, IJ and JK time intervals within a typical BCG waveform were calculated. The proposed BCG dynamical model is composed of three coupled ordinary differential equations i.e. Gaussian kernel functions. The synthetic BCG signal which can illustrate beat-to-beat trajectory variation in BCG morphology was then outputted from the dynamical model. This model was evaluated by adding white and Gaussian noises artificially on several normal and real BCGs and synthetic BCGs, and the output SNR and morphology changes of the filter outputs was investigated. Results show that the dynamical model can be effectively used as a valid application in synthetic bio-signal generation and nonlinear system processing. In the near future, this novel model can be utilized to evaluate bio-signal processing algorithms and help extract clinical views from the real BCG signal. Master of Science (Signal Processing) 2016-08-22T07:00:08Z 2016-08-22T07:00:08Z 2016 Thesis http://hdl.handle.net/10356/68982 en 68 p. application/pdf
spellingShingle DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Zhang, Qifan
A dynamical model for generating synthetic ballistocardiogram signals based on Extended Kalman Filter (EKF)
title A dynamical model for generating synthetic ballistocardiogram signals based on Extended Kalman Filter (EKF)
title_full A dynamical model for generating synthetic ballistocardiogram signals based on Extended Kalman Filter (EKF)
title_fullStr A dynamical model for generating synthetic ballistocardiogram signals based on Extended Kalman Filter (EKF)
title_full_unstemmed A dynamical model for generating synthetic ballistocardiogram signals based on Extended Kalman Filter (EKF)
title_short A dynamical model for generating synthetic ballistocardiogram signals based on Extended Kalman Filter (EKF)
title_sort dynamical model for generating synthetic ballistocardiogram signals based on extended kalman filter ekf
topic DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
url http://hdl.handle.net/10356/68982
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