Missing data problem in random electrocardiogram signal processing

Basically, signals are the entities that convey information and biomedical signals are the signals that carry information about the physiological process of organisms. Electrocardiogram (ECG) signal or known as heart signal is the signal that contains information about electrical activities in the h...

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Main Author:	Gan, Thiam Yee
Format:	Thesis
Language:	English
Published:	2014
Subjects:	RC Internal medicine
Online Access:	http://eprints.utm.my/id/eprint/48531/1/GanThiamYeeMFS2014.pdf
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spelling	my-utm-ep.485312017-07-27T04:59:18Z Missing data problem in random electrocardiogram signal processing 2014 Gan, Thiam Yee RC Internal medicine Basically, signals are the entities that convey information and biomedical signals are the signals that carry information about the physiological process of organisms. Electrocardiogram (ECG) signal or known as heart signal is the signal that contains information about electrical activities in the heart. Since physiological signal are generated at low values and devices advancements are not sufficient to detect these small values perfectly, these signal tends to be missing from the record. As the noise interferes the signal at the same time, raw signal is practically unreliable to be interpreted directly. Hence, the random signal processing is required to obtain the signal as precise as possible. In this study, the missing probabilities of signal missingness were set to 0.1 at high values and 0.3 at low values. The noise to be reduced is Gaussian noise with zero mean and standard deviation 0.01 mV. A few methods have been applied to estimate the missing signal, including single mean imputation, empirical conditional mean imputation and Holt-Winters exponential smoothing. For noise filtering, the approach used is the Finite Impulse Response (FIR) Wiener filter. The study finds that the empirical conditional mean imputation is the best method among the three to estimate missing signal due to its accuracy, adequacy and simplicity. However, it appears that the FIR Wiener filter does not compatible with the estimation from empirical conditional mean imputation and does not further improve the signal quality by removing noise in general. 2014 Thesis http://eprints.utm.my/id/eprint/48531/ http://eprints.utm.my/id/eprint/48531/1/GanThiamYeeMFS2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:78023?queryType=vitalDismax&query=Missing+data+problem+in+random+electrocardiogram+signal+processing&public=true masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	RC Internal medicine
spellingShingle	RC Internal medicine Gan, Thiam Yee Missing data problem in random electrocardiogram signal processing
description	Basically, signals are the entities that convey information and biomedical signals are the signals that carry information about the physiological process of organisms. Electrocardiogram (ECG) signal or known as heart signal is the signal that contains information about electrical activities in the heart. Since physiological signal are generated at low values and devices advancements are not sufficient to detect these small values perfectly, these signal tends to be missing from the record. As the noise interferes the signal at the same time, raw signal is practically unreliable to be interpreted directly. Hence, the random signal processing is required to obtain the signal as precise as possible. In this study, the missing probabilities of signal missingness were set to 0.1 at high values and 0.3 at low values. The noise to be reduced is Gaussian noise with zero mean and standard deviation 0.01 mV. A few methods have been applied to estimate the missing signal, including single mean imputation, empirical conditional mean imputation and Holt-Winters exponential smoothing. For noise filtering, the approach used is the Finite Impulse Response (FIR) Wiener filter. The study finds that the empirical conditional mean imputation is the best method among the three to estimate missing signal due to its accuracy, adequacy and simplicity. However, it appears that the FIR Wiener filter does not compatible with the estimation from empirical conditional mean imputation and does not further improve the signal quality by removing noise in general.
format	Thesis
qualification_level	Master's degree
author	Gan, Thiam Yee
author_facet	Gan, Thiam Yee
author_sort	Gan, Thiam Yee
title	Missing data problem in random electrocardiogram signal processing
title_short	Missing data problem in random electrocardiogram signal processing
title_full	Missing data problem in random electrocardiogram signal processing
title_fullStr	Missing data problem in random electrocardiogram signal processing
title_full_unstemmed	Missing data problem in random electrocardiogram signal processing
title_sort	missing data problem in random electrocardiogram signal processing
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2014
url	http://eprints.utm.my/id/eprint/48531/1/GanThiamYeeMFS2014.pdf
_version_	1747817413149196288

Missing data problem in random electrocardiogram signal processing

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