Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study

[EN] Bipolar Disorder (BD) is an illness with high prevalence and a huge social and economic impact. It is recurrent, with a long-term evolution in most cases. Early treatment and continuous monitoring have proven to be very effective in mitigating the causes and consequences of BD. However, no tool...

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Detalles Bibliográficos
Autores: Cuesta Frau, David|||0000-0002-0076-0515, Schneider, Jesper W., Bakstein, E., Vostatek, P., Spaniel, F., Novák, Daniel
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/231182
Acceso en línea:https://riunet.upv.es/handle/10251/231182
Access Level:acceso abierto
Palabra clave:Bipolar disorder
Actigraphy
Sample entropy
Permutation entropy
Slope entropy
Time series classification
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spelling Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility StudyCuesta Frau, David|||0000-0002-0076-0515Schneider, Jesper W.Bakstein, E.Vostatek, P.Spaniel, F.Novák, DanielBipolar disorderActigraphySample entropyPermutation entropySlope entropyTime series classification[EN] Bipolar Disorder (BD) is an illness with high prevalence and a huge social and economic impact. It is recurrent, with a long-term evolution in most cases. Early treatment and continuous monitoring have proven to be very effective in mitigating the causes and consequences of BD. However, no tools are currently available for a massive and semi-automatic BD patient monitoring and control. Taking advantage of recent technological developments in the field of wearables, this paper studies the feasibility of a BD episodes classification analysis while using entropy measures, an approach successfully applied in a myriad of other physiological frameworks. This is a very difficult task, since actigraphy records are highly non-stationary and corrupted with artifacts (no activity). The method devised uses a preprocessing stage to extract epochs of activity, and then applies a quantification measure, Slope Entropy, recently proposed, which outperforms the most common entropy measures used in biomedical time series. The results confirm the feasibility of the approach proposed, since the three states that are involved in BD, depression, mania, and remission, can be significantly distinguished.The work of J.S. has been supported by students grant agency of the Czech Technical University in Prague (grant number SGS19/171/OHK3/3T/13).MDPI AGDepartamento de Informática de Sistemas y ComputadoresEscuela Politécnica Superior de AlcoyCzech Technical University in PragueRepositorio Institucional de la Universitat Politècnica de València Riunet20202020-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/231182reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengCzech Technical University in Prague https://doi.org/10.13039/100007655 SGS19%2F171%2FOHK3%2F3T%2F13open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2311822026-06-13T07:49:27Z
dc.title.none.fl_str_mv Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study
title Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study
spellingShingle Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study
Cuesta Frau, David|||0000-0002-0076-0515
Bipolar disorder
Actigraphy
Sample entropy
Permutation entropy
Slope entropy
Time series classification
title_short Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study
title_full Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study
title_fullStr Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study
title_full_unstemmed Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study
title_sort Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study
dc.creator.none.fl_str_mv Cuesta Frau, David|||0000-0002-0076-0515
Schneider, Jesper W.
Bakstein, E.
Vostatek, P.
Spaniel, F.
Novák, Daniel
author Cuesta Frau, David|||0000-0002-0076-0515
author_facet Cuesta Frau, David|||0000-0002-0076-0515
Schneider, Jesper W.
Bakstein, E.
Vostatek, P.
Spaniel, F.
Novák, Daniel
author_role author
author2 Schneider, Jesper W.
Bakstein, E.
Vostatek, P.
Spaniel, F.
Novák, Daniel
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Informática de Sistemas y Computadores
Escuela Politécnica Superior de Alcoy
Czech Technical University in Prague
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Bipolar disorder
Actigraphy
Sample entropy
Permutation entropy
Slope entropy
Time series classification
topic Bipolar disorder
Actigraphy
Sample entropy
Permutation entropy
Slope entropy
Time series classification
description [EN] Bipolar Disorder (BD) is an illness with high prevalence and a huge social and economic impact. It is recurrent, with a long-term evolution in most cases. Early treatment and continuous monitoring have proven to be very effective in mitigating the causes and consequences of BD. However, no tools are currently available for a massive and semi-automatic BD patient monitoring and control. Taking advantage of recent technological developments in the field of wearables, this paper studies the feasibility of a BD episodes classification analysis while using entropy measures, an approach successfully applied in a myriad of other physiological frameworks. This is a very difficult task, since actigraphy records are highly non-stationary and corrupted with artifacts (no activity). The method devised uses a preprocessing stage to extract epochs of activity, and then applies a quantification measure, Slope Entropy, recently proposed, which outperforms the most common entropy measures used in biomedical time series. The results confirm the feasibility of the approach proposed, since the three states that are involved in BD, depression, mania, and remission, can be significantly distinguished.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-11-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/231182
url https://riunet.upv.es/handle/10251/231182
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Czech Technical University in Prague https://doi.org/10.13039/100007655 SGS19%2F171%2FOHK3%2F3T%2F13
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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