Assessment of the state of health by the measurement of a set of biophysiological signals
Autor(a) principal: | |
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Data de Publicação: | 2010 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.6/2310 |
Resumo: | The dissertation studies the estimation of the degree of self-similarity and entropy of Shannon of several real electrocardiography (ECG) signals for healthy and non-healthy humans. The goal of the dissertation is to create a starting point algorithm which allows distinguishing between healthy and non-healthy subjects and can be used as a basis for further study of a diagnosis algorithm, necessarily more complex. We used a novel Hurst parameter estimation algorithm based on the Embedded Branching Process, termed modified Embedded Branching Process algorithm. The algorithm for estimation of entropy was based on Shannon‟s entropy. Both algorithms were applied on the spatial distribution of ECG signals in a windowed manner. The studied signals were retrieved from the Physionet website, where they are diagnosed as normal or as having certain pathologies. The results presented for the Hurst parameter estimation allow us to confirm the results already published on the temporal self-similarity of ECG signals, this time for its spatial distribution. We also conclude that the non-self similar signals belong to non-healthy subjects. The results obtained for entropy estimation on the spatial distribution of ECG signals also allowed a comparison between healthy and non-healthy systems. We obtained high entropy estimates both for healthy and non-healthy subjects; nevertheless, non-healthy subjects show higher variability of Shannon‟s entropy than healthy ones. |
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Assessment of the state of health by the measurement of a set of biophysiological signalsElectrocardiograma - Doença cardíacaEntropia de ShanonElectrocardiograma - Parâmetro de HurstDoença cardíaca - Aspectos biofisiológicosThe dissertation studies the estimation of the degree of self-similarity and entropy of Shannon of several real electrocardiography (ECG) signals for healthy and non-healthy humans. The goal of the dissertation is to create a starting point algorithm which allows distinguishing between healthy and non-healthy subjects and can be used as a basis for further study of a diagnosis algorithm, necessarily more complex. We used a novel Hurst parameter estimation algorithm based on the Embedded Branching Process, termed modified Embedded Branching Process algorithm. The algorithm for estimation of entropy was based on Shannon‟s entropy. Both algorithms were applied on the spatial distribution of ECG signals in a windowed manner. The studied signals were retrieved from the Physionet website, where they are diagnosed as normal or as having certain pathologies. The results presented for the Hurst parameter estimation allow us to confirm the results already published on the temporal self-similarity of ECG signals, this time for its spatial distribution. We also conclude that the non-self similar signals belong to non-healthy subjects. The results obtained for entropy estimation on the spatial distribution of ECG signals also allowed a comparison between healthy and non-healthy systems. We obtained high entropy estimates both for healthy and non-healthy subjects; nevertheless, non-healthy subjects show higher variability of Shannon‟s entropy than healthy ones.A dissertação estuda a estimativa do grau de auto-semelhança e da entropia de Shannon de vários sinais reais de electrocardiograma (ECG) obtidos em humanos saudáveis e não saudáveis. O objectivo da dissertação é criar um algoritmo inicial que permita distinguir entre indivíduos saudáveis e não saudáveis e que possa ser usado como base para o estudo de um posterior algoritmo de diagnóstico, necessariamente mais complexo. Utilizamos um algoritmo novo para estimativa do parâmetro de Hurst baseado no Embedded Branching Process, denominado algoritmo modified Embedded Branching Process. A entropia foi estimada através da entropia de Shannon. Ambos algoritmos foram aplicados sob a distribuição espacial dos sinais ECG numa forma de janela. Os sinais estudados foram retirados do website Physionet, onde estão diagnosticados como normais ou possuindo uma determinada patologia. Os resultados apresentados para a estimativa do parâmetro de Hurst permitem confirmar resultados já publicados sobre a auto-semelhança temporal dos sinais ECG, desta vez para a sua distribuição espacial. Também se concluí que os sinais não auto-semelhantes correspondem a indivíduos não saudáveis. Os resultados obtidos na estimativa da entropia para a distribuição espacial dos sinais de ECG também permitiram uma comparação entre sistemas saudáveis e não saudáveis. Obtiveram-se estimativas de entropia elevadas quer para indivíduos saudáveis quer para indivíduos não saudáveis; no entanto, os indivíduos não saudáveis mostram uma maior variabilidade da entropia de Shannon em relação aos saudáveis.Santos, Nuno Manuel Garcia dosSousa, Miguel Castelo Branco Craveiro deuBibliorumSousa, Paula Sofia Barros de2014-09-23T09:27:54Z201020102010-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.6/2310enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-01-16T11:40:07ZPortal AgregadorONG |
dc.title.none.fl_str_mv |
Assessment of the state of health by the measurement of a set of biophysiological signals |
title |
Assessment of the state of health by the measurement of a set of biophysiological signals |
spellingShingle |
Assessment of the state of health by the measurement of a set of biophysiological signals Sousa, Paula Sofia Barros de Electrocardiograma - Doença cardíaca Entropia de Shanon Electrocardiograma - Parâmetro de Hurst Doença cardíaca - Aspectos biofisiológicos |
title_short |
Assessment of the state of health by the measurement of a set of biophysiological signals |
title_full |
Assessment of the state of health by the measurement of a set of biophysiological signals |
title_fullStr |
Assessment of the state of health by the measurement of a set of biophysiological signals |
title_full_unstemmed |
Assessment of the state of health by the measurement of a set of biophysiological signals |
title_sort |
Assessment of the state of health by the measurement of a set of biophysiological signals |
author |
Sousa, Paula Sofia Barros de |
author_facet |
Sousa, Paula Sofia Barros de |
author_role |
author |
dc.contributor.none.fl_str_mv |
Santos, Nuno Manuel Garcia dos Sousa, Miguel Castelo Branco Craveiro de uBibliorum |
dc.contributor.author.fl_str_mv |
Sousa, Paula Sofia Barros de |
dc.subject.por.fl_str_mv |
Electrocardiograma - Doença cardíaca Entropia de Shanon Electrocardiograma - Parâmetro de Hurst Doença cardíaca - Aspectos biofisiológicos |
topic |
Electrocardiograma - Doença cardíaca Entropia de Shanon Electrocardiograma - Parâmetro de Hurst Doença cardíaca - Aspectos biofisiológicos |
description |
The dissertation studies the estimation of the degree of self-similarity and entropy of Shannon of several real electrocardiography (ECG) signals for healthy and non-healthy humans. The goal of the dissertation is to create a starting point algorithm which allows distinguishing between healthy and non-healthy subjects and can be used as a basis for further study of a diagnosis algorithm, necessarily more complex. We used a novel Hurst parameter estimation algorithm based on the Embedded Branching Process, termed modified Embedded Branching Process algorithm. The algorithm for estimation of entropy was based on Shannon‟s entropy. Both algorithms were applied on the spatial distribution of ECG signals in a windowed manner. The studied signals were retrieved from the Physionet website, where they are diagnosed as normal or as having certain pathologies. The results presented for the Hurst parameter estimation allow us to confirm the results already published on the temporal self-similarity of ECG signals, this time for its spatial distribution. We also conclude that the non-self similar signals belong to non-healthy subjects. The results obtained for entropy estimation on the spatial distribution of ECG signals also allowed a comparison between healthy and non-healthy systems. We obtained high entropy estimates both for healthy and non-healthy subjects; nevertheless, non-healthy subjects show higher variability of Shannon‟s entropy than healthy ones. |
publishDate |
2010 |
dc.date.none.fl_str_mv |
2010 2010 2010-01-01T00:00:00Z 2014-09-23T09:27:54Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.6/2310 |
url |
http://hdl.handle.net/10400.6/2310 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
|
repository.mail.fl_str_mv |
|
_version_ |
1777301767506100224 |