Validation of psychophysiological measures for caffeine oral films characterization by machine learning approaches

Detalhes bibliográficos
Autor(a) principal: Batista, Patrícia
Data de Publicação: 2022
Outros Autores: Rodrigues, Pedro Miguel, Ferreira, Miguel, Moreno, Ana, Silva, Gabriel, Alves, Marco, Pintado, Manuela, Oliveira-Silva, Patrícia
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.14/37233
Resumo: Background: The oral films are a new delivery system that can carry several molecules, such as neuromodulator molecules, including caffeine. These delivery systems have been developed and characterized by pharmacokinetics assays. However, new methodologies, such as psychophysiological measures, can complement their characterization. This study presents a new protocol with psychophysiological parameters to characterize the oral film delivery systems based on a caffeine model. (2) Methods: Thirteen volunteers (61.5% females and 38.5% males) consumed caffeine oral films and placebo oral films (in different moments and without knowing the product). Electrocardiogram (ECG), electrodermal (EDA), and respiratory frequency (RF) data were monitored for 45 min. For the data analysis, the MATLAB environment was used to develop the analysis program. The ECG, EDA, and RF signals were digitally filtered and processed, using a windowing process, for feature extraction and an energy mean value for 5 min segments. Then, the data were computed and presented to the entries of a set of Machine Learning algorithms. Finally, a data statistical analysis was carried out using SPSS. (3) Results: Compared with placebo, caffeine oral films led to a significant increase in power energy in the signal spectrum of heart rate, skin conductance, and respiratory activity. In addition, the ECG time-series power energy activity revealed a better capacity to detect caffeine activity over time than the other physiological modalities. There was no significant change for the female or male gender. (4) Conclusions: The protocol developed, and the psychophysiological methodology used to characterize the delivery system profile were efficient to characterize the drug delivery profile of the caffeine. This is a non-invasive, cheap, and easy method to apply, can be used to determine the neuromodulator drugs delivery profile, and can be implemented in the future.
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spelling Validation of psychophysiological measures for caffeine oral films characterization by machine learning approachesOral filmsDelivery systemsCaffeineElectrodermal activityElectrocardiogramRespiratory activityBackground: The oral films are a new delivery system that can carry several molecules, such as neuromodulator molecules, including caffeine. These delivery systems have been developed and characterized by pharmacokinetics assays. However, new methodologies, such as psychophysiological measures, can complement their characterization. This study presents a new protocol with psychophysiological parameters to characterize the oral film delivery systems based on a caffeine model. (2) Methods: Thirteen volunteers (61.5% females and 38.5% males) consumed caffeine oral films and placebo oral films (in different moments and without knowing the product). Electrocardiogram (ECG), electrodermal (EDA), and respiratory frequency (RF) data were monitored for 45 min. For the data analysis, the MATLAB environment was used to develop the analysis program. The ECG, EDA, and RF signals were digitally filtered and processed, using a windowing process, for feature extraction and an energy mean value for 5 min segments. Then, the data were computed and presented to the entries of a set of Machine Learning algorithms. Finally, a data statistical analysis was carried out using SPSS. (3) Results: Compared with placebo, caffeine oral films led to a significant increase in power energy in the signal spectrum of heart rate, skin conductance, and respiratory activity. In addition, the ECG time-series power energy activity revealed a better capacity to detect caffeine activity over time than the other physiological modalities. There was no significant change for the female or male gender. (4) Conclusions: The protocol developed, and the psychophysiological methodology used to characterize the delivery system profile were efficient to characterize the drug delivery profile of the caffeine. This is a non-invasive, cheap, and easy method to apply, can be used to determine the neuromodulator drugs delivery profile, and can be implemented in the future.Veritati - Repositório Institucional da Universidade Católica PortuguesaBatista, PatríciaRodrigues, Pedro MiguelFerreira, MiguelMoreno, AnaSilva, GabrielAlves, MarcoPintado, ManuelaOliveira-Silva, Patrícia2022-04-01T15:09:38Z2022-03-112022-03-11T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.14/37233eng2306-535410.3390/bioengineering903011485126753866PMC894533735324803000776165500001info: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:RCAAP2023-07-12T17:42:40Zoai:repositorio.ucp.pt:10400.14/37233Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:30:16.809236Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Validation of psychophysiological measures for caffeine oral films characterization by machine learning approaches
title Validation of psychophysiological measures for caffeine oral films characterization by machine learning approaches
spellingShingle Validation of psychophysiological measures for caffeine oral films characterization by machine learning approaches
Batista, Patrícia
Oral films
Delivery systems
Caffeine
Electrodermal activity
Electrocardiogram
Respiratory activity
title_short Validation of psychophysiological measures for caffeine oral films characterization by machine learning approaches
title_full Validation of psychophysiological measures for caffeine oral films characterization by machine learning approaches
title_fullStr Validation of psychophysiological measures for caffeine oral films characterization by machine learning approaches
title_full_unstemmed Validation of psychophysiological measures for caffeine oral films characterization by machine learning approaches
title_sort Validation of psychophysiological measures for caffeine oral films characterization by machine learning approaches
author Batista, Patrícia
author_facet Batista, Patrícia
Rodrigues, Pedro Miguel
Ferreira, Miguel
Moreno, Ana
Silva, Gabriel
Alves, Marco
Pintado, Manuela
Oliveira-Silva, Patrícia
author_role author
author2 Rodrigues, Pedro Miguel
Ferreira, Miguel
Moreno, Ana
Silva, Gabriel
Alves, Marco
Pintado, Manuela
Oliveira-Silva, Patrícia
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Veritati - Repositório Institucional da Universidade Católica Portuguesa
dc.contributor.author.fl_str_mv Batista, Patrícia
Rodrigues, Pedro Miguel
Ferreira, Miguel
Moreno, Ana
Silva, Gabriel
Alves, Marco
Pintado, Manuela
Oliveira-Silva, Patrícia
dc.subject.por.fl_str_mv Oral films
Delivery systems
Caffeine
Electrodermal activity
Electrocardiogram
Respiratory activity
topic Oral films
Delivery systems
Caffeine
Electrodermal activity
Electrocardiogram
Respiratory activity
description Background: The oral films are a new delivery system that can carry several molecules, such as neuromodulator molecules, including caffeine. These delivery systems have been developed and characterized by pharmacokinetics assays. However, new methodologies, such as psychophysiological measures, can complement their characterization. This study presents a new protocol with psychophysiological parameters to characterize the oral film delivery systems based on a caffeine model. (2) Methods: Thirteen volunteers (61.5% females and 38.5% males) consumed caffeine oral films and placebo oral films (in different moments and without knowing the product). Electrocardiogram (ECG), electrodermal (EDA), and respiratory frequency (RF) data were monitored for 45 min. For the data analysis, the MATLAB environment was used to develop the analysis program. The ECG, EDA, and RF signals were digitally filtered and processed, using a windowing process, for feature extraction and an energy mean value for 5 min segments. Then, the data were computed and presented to the entries of a set of Machine Learning algorithms. Finally, a data statistical analysis was carried out using SPSS. (3) Results: Compared with placebo, caffeine oral films led to a significant increase in power energy in the signal spectrum of heart rate, skin conductance, and respiratory activity. In addition, the ECG time-series power energy activity revealed a better capacity to detect caffeine activity over time than the other physiological modalities. There was no significant change for the female or male gender. (4) Conclusions: The protocol developed, and the psychophysiological methodology used to characterize the delivery system profile were efficient to characterize the drug delivery profile of the caffeine. This is a non-invasive, cheap, and easy method to apply, can be used to determine the neuromodulator drugs delivery profile, and can be implemented in the future.
publishDate 2022
dc.date.none.fl_str_mv 2022-04-01T15:09:38Z
2022-03-11
2022-03-11T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.14/37233
url http://hdl.handle.net/10400.14/37233
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 2306-5354
10.3390/bioengineering9030114
85126753866
PMC8945337
35324803
000776165500001
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dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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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 Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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