Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor

Detalhes bibliográficos
Autor(a) principal: Pimentel, Angela Bairos
Data de Publicação: 2012
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Texto Completo: http://hdl.handle.net/10362/8443
Resumo: Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica
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spelling Algorithm for the Parkinson’s disease behavioural models characterization using a biosensorPDZebrafishMOBSBehaviourMachine learningZero crossing rateDissertação para obtenção do Grau de Mestre em Engenharia BiomédicaThe neurodegenerative disease, Parkinson’s Disease (PD) constitutes a major health problem in the modern world, and its impact on public health and society is expected to increase with the ongoing ageing of the human population. This disease is characterized by motor and non-motor manifestations that are progressive and ultimately refractory to therapeutic interventions. The degeneration of dopaminergic neurons emanating from the substantia nigra is largely responsible for the motor manifestations. Thus, understanding the behaviour related to this disease is an added value for the diagnosis and treatment of PD. Also, in vivo models are essential tools for deciphering the molecular mechanisms underpinning the neurodegenerative process. Zebrafish has several features that make this species a good candidate to study PD. In particular, the occurrence of behavioural phenotypes of treated animals with neurotoxin drugs that mimic the disease has been investigated. And, an electric biosensor, Marine On-line Biomonitor System (MOBS) is being used for the real-time quantification of such behaviour. This equipment allows quantifying the fish movements through signal processing algorithms. Specifically, the algorithm is used for the evaluation of fish locomotion detected by a series of bursts in the domain of MOBS that correspond to the zebrafish tail-flip activity. In this thesis we proceeded to the development of an algorithm affording a electrical signal discrimination between "healthy" and "ill" zebrafish and consequently improving the detection of parkinsonism-like phenotypes in zebrafish. The first approach was the improvement of the existent algorithm. However, the first analysis failed to distinguish between different behavioural phenotypes when fish were treated with the neurotoxin 6-hydroxydopamine (6-OHDA). Consequently, we generated a new algorithm based on Machine Learning techniques. As a result, the novel algorithm provided a classification over the health condition of the fish, if the same is "healthy" or "ill" with its respective probability and the level of activity of the fish in number of tail-flips per minute. The method Support Vector Machine (SVM)was useful for the classification of the fish events. The zero crossing rate parameter was used for the characterization of the swimming activities. The algorithm was also integrated in the platform Open Signals, and for a faster evaluation of the signals, the algorithm implementation included parallel programming methods. This algorithm is a useful tool to study behaviour in zebrafish. Not only it will allow a more realistic study over the PD research area but also test and assess new drugs that use zebrafish as animal model.Faculdade de Ciências e TecnologiaGamboa, HugoCorreia, AnaRUNPimentel, Angela Bairos2013-01-07T16:16:59Z20122012-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/8443enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:12:05Zoai:run.unl.pt:10362/8443Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T16:43:05.345868Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor
title Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor
spellingShingle Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor
Pimentel, Angela Bairos
PD
Zebrafish
MOBS
Behaviour
Machine learning
Zero crossing rate
title_short Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor
title_full Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor
title_fullStr Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor
title_full_unstemmed Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor
title_sort Algorithm for the Parkinson’s disease behavioural models characterization using a biosensor
author Pimentel, Angela Bairos
author_facet Pimentel, Angela Bairos
author_role author
dc.contributor.none.fl_str_mv Gamboa, Hugo
Correia, Ana
RUN
dc.contributor.author.fl_str_mv Pimentel, Angela Bairos
dc.subject.por.fl_str_mv PD
Zebrafish
MOBS
Behaviour
Machine learning
Zero crossing rate
topic PD
Zebrafish
MOBS
Behaviour
Machine learning
Zero crossing rate
description Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-01-01T00:00:00Z
2013-01-07T16:16:59Z
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/10362/8443
url http://hdl.handle.net/10362/8443
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.publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596137923149824

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