Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis

[EN] Fever is a readily measurable physiological response that has been used in medicine for centuries. However, the information provided has been greatly limited by a plain thresholding approach, overlooking the additional information provided by temporal variations and temperature values below suc...

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Autores: Cuesta Frau, David|||0000-0002-0076-0515, Dakappa, Pradeepa H., Mahabala, Chakrapani, Gupta, Arjun R.
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:10251/231181
Acceso en línea:https://riunet.upv.es/handle/10251/231181
Access Level:acceso abierto
Palabra clave:Slope Entropy
Time series classification
Body temperature
Fever
Matthews Correlation Coefficient
Malaria
Dengue
Differential diagnosis
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spelling Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential DiagnosisCuesta Frau, David|||0000-0002-0076-0515Dakappa, Pradeepa H.Mahabala, ChakrapaniGupta, Arjun R.Slope EntropyTime series classificationBody temperatureFeverMatthews Correlation CoefficientMalariaDengueDifferential diagnosis[EN] Fever is a readily measurable physiological response that has been used in medicine for centuries. However, the information provided has been greatly limited by a plain thresholding approach, overlooking the additional information provided by temporal variations and temperature values below such threshold that are also representative of the subject status. In this paper, we propose to utilize continuous body temperature time series of patients that developed a fever, in order to apply a method capable of diagnosing the specific underlying fever cause only by means of a pattern relative frequency analysis. This analysis was based on a recently proposed measure, Slope Entropy, applied to a variety of records coming from dengue and malaria patients, among other fever diseases. After an input parameter customization, a classification analysis of malaria and dengue records took place, quantified by the Matthews Correlation Coefficient. This classification yielded a high accuracy, with more than 90% of the records correctly labelled in some cases, demonstrating the feasibility of the approach proposed. This approach, after further studies, or combined with more measures such as Sample Entropy, is certainly very promising in becoming an early diagnosis tool based solely on body temperature temporal patterns, which is of great interest in the current Covid-19 pandemic scenario.MDPI AGDepartamento de Informática de Sistemas y ComputadoresEscuela Politécnica Superior de AlcoyRepositorio Institucional de la Universitat Politècnica de València Riunet20202020-09-15journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/231181reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2311812026-06-13T07:49:27Z
dc.title.none.fl_str_mv Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis
title Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis
spellingShingle Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis
Cuesta Frau, David|||0000-0002-0076-0515
Slope Entropy
Time series classification
Body temperature
Fever
Matthews Correlation Coefficient
Malaria
Dengue
Differential diagnosis
title_short Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis
title_full Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis
title_fullStr Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis
title_full_unstemmed Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis
title_sort Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis
dc.creator.none.fl_str_mv Cuesta Frau, David|||0000-0002-0076-0515
Dakappa, Pradeepa H.
Mahabala, Chakrapani
Gupta, Arjun R.
author Cuesta Frau, David|||0000-0002-0076-0515
author_facet Cuesta Frau, David|||0000-0002-0076-0515
Dakappa, Pradeepa H.
Mahabala, Chakrapani
Gupta, Arjun R.
author_role author
author2 Dakappa, Pradeepa H.
Mahabala, Chakrapani
Gupta, Arjun R.
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Informática de Sistemas y Computadores
Escuela Politécnica Superior de Alcoy
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Slope Entropy
Time series classification
Body temperature
Fever
Matthews Correlation Coefficient
Malaria
Dengue
Differential diagnosis
topic Slope Entropy
Time series classification
Body temperature
Fever
Matthews Correlation Coefficient
Malaria
Dengue
Differential diagnosis
description [EN] Fever is a readily measurable physiological response that has been used in medicine for centuries. However, the information provided has been greatly limited by a plain thresholding approach, overlooking the additional information provided by temporal variations and temperature values below such threshold that are also representative of the subject status. In this paper, we propose to utilize continuous body temperature time series of patients that developed a fever, in order to apply a method capable of diagnosing the specific underlying fever cause only by means of a pattern relative frequency analysis. This analysis was based on a recently proposed measure, Slope Entropy, applied to a variety of records coming from dengue and malaria patients, among other fever diseases. After an input parameter customization, a classification analysis of malaria and dengue records took place, quantified by the Matthews Correlation Coefficient. This classification yielded a high accuracy, with more than 90% of the records correctly labelled in some cases, demonstrating the feasibility of the approach proposed. This approach, after further studies, or combined with more measures such as Sample Entropy, is certainly very promising in becoming an early diagnosis tool based solely on body temperature temporal patterns, which is of great interest in the current Covid-19 pandemic scenario.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-09-15
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/10251/231181
url https://riunet.upv.es/handle/10251/231181
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
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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