Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample Entropy
Fever is a common symptom of many diseases. Fever temporal patterns can be different depending on the specific pathology. Differentiation of diseases based on multiple mathematical features and visual observations has been recently studied in the scientific literature. However, the classification of...
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| Format: | Article |
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AIMS Press
2020-01-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2020013?viewType=HTML |
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| author | David Cuesta-Frau Pau Miró-Martínez Sandra Oltra-Crespo Antonio Molina-Picó Pradeepa H. Dakappa Chakrapani Mahabala Borja Vargas Paula González |
| author_facet | David Cuesta-Frau Pau Miró-Martínez Sandra Oltra-Crespo Antonio Molina-Picó Pradeepa H. Dakappa Chakrapani Mahabala Borja Vargas Paula González |
| author_sort | David Cuesta-Frau |
| collection | DOAJ |
| description | Fever is a common symptom of many diseases. Fever temporal patterns can be different depending on the specific pathology. Differentiation of diseases based on multiple mathematical features and visual observations has been recently studied in the scientific literature. However, the classification of diseases using a single mathematical feature has not been tried yet. The aim of the present study is to assess the feasibility of classifying diseases based on fever patterns using a single mathematical feature, specifically an entropy measure, Sample Entropy. This was an observational study. Analysis was carried out using 103 patients, 24 hour continuous tympanic temperature data. Sample Entropy feature was extracted from temperature data of patients. Grouping of diseases (infectious, tuberculosis, non-tuberculosis, and dengue fever) was made based on physicians diagnosis and laboratory findings. The quantitative results confirm the feasibility of the approach proposed, with an overall classification accuracy close to 70%, and the capability of finding significant differences for all the classes studied. %An abstract is a brief of the paper; the abstract should not contain references, the text of the abstract section should be in 12 point normal Times New Roman. |
| first_indexed | 2024-12-21T19:55:56Z |
| format | Article |
| id | doaj.art-9cac169aad304a108649c47409679d53 |
| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-12-21T19:55:56Z |
| publishDate | 2020-01-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-9cac169aad304a108649c47409679d532022-12-21T18:52:05ZengAIMS PressMathematical Biosciences and Engineering1551-00182020-01-0117123524910.3934/mbe.2020013Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample EntropyDavid Cuesta-Frau0Pau Miró-Martínez1Sandra Oltra-Crespo2Antonio Molina-Picó3Pradeepa H. Dakappa4Chakrapani Mahabala5Borja Vargas6Paula González71. Technological Institute of Informatics(ITI), Universitat Politècnica de València, Campus Alcoi, Plaza Ferrándiz y Carbonell, 2, 03801, Alcoi, Spain 2. Innovatec Sensorización y Comunicación S. L., Avda. Elx, 3, 03801, Alcoi, Spain3. Department of Statistics, Universitat Politècnica de València, Campus Alcoi, Plaza Ferrándiz y Carbonell, 2, 03801, Alcoi, Spain1. Technological Institute of Informatics(ITI), Universitat Politècnica de València, Campus Alcoi, Plaza Ferrándiz y Carbonell, 2, 03801, Alcoi, Spain1. Technological Institute of Informatics(ITI), Universitat Politècnica de València, Campus Alcoi, Plaza Ferrándiz y Carbonell, 2, 03801, Alcoi, Spain 2. Innovatec Sensorización y Comunicación S. L., Avda. Elx, 3, 03801, Alcoi, Spain4. Department of Pharmacology, MVJ Medical College and Research Hospital, Dandupalya, Hoskote, Karnataka, India5. Department of General Medicine, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India6. Department of Internal Medicine, Móstoles Teaching Hospital, Móstoles, 28935, Madrid, Spain6. Department of Internal Medicine, Móstoles Teaching Hospital, Móstoles, 28935, Madrid, SpainFever is a common symptom of many diseases. Fever temporal patterns can be different depending on the specific pathology. Differentiation of diseases based on multiple mathematical features and visual observations has been recently studied in the scientific literature. However, the classification of diseases using a single mathematical feature has not been tried yet. The aim of the present study is to assess the feasibility of classifying diseases based on fever patterns using a single mathematical feature, specifically an entropy measure, Sample Entropy. This was an observational study. Analysis was carried out using 103 patients, 24 hour continuous tympanic temperature data. Sample Entropy feature was extracted from temperature data of patients. Grouping of diseases (infectious, tuberculosis, non-tuberculosis, and dengue fever) was made based on physicians diagnosis and laboratory findings. The quantitative results confirm the feasibility of the approach proposed, with an overall classification accuracy close to 70%, and the capability of finding significant differences for all the classes studied. %An abstract is a brief of the paper; the abstract should not contain references, the text of the abstract section should be in 12 point normal Times New Roman.https://www.aimspress.com/article/doi/10.3934/mbe.2020013?viewType=HTMLfevertime series classificationtuberculosisdenguediagnostic aidssample entropytrace segmentation |
| spellingShingle | David Cuesta-Frau Pau Miró-Martínez Sandra Oltra-Crespo Antonio Molina-Picó Pradeepa H. Dakappa Chakrapani Mahabala Borja Vargas Paula González Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample Entropy Mathematical Biosciences and Engineering fever time series classification tuberculosis dengue diagnostic aids sample entropy trace segmentation |
| title | Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample Entropy |
| title_full | Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample Entropy |
| title_fullStr | Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample Entropy |
| title_full_unstemmed | Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample Entropy |
| title_short | Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample Entropy |
| title_sort | classification of fever patterns using a single extracted entropy feature a feasibility study based on sample entropy |
| topic | fever time series classification tuberculosis dengue diagnostic aids sample entropy trace segmentation |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2020013?viewType=HTML |
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