Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample Entropy

David Cuesta-Frau, Pau Miró-Martínez, Sandra Oltra-Crespo, Antonio Molina-Picó, Pradeepa H. Dakappa, Chakrapani Mahabala, Borja Vargas, Paula González

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)235-249
Number of pages15
JournalMathematical biosciences and engineering : MBE
Issue number1
Publication statusPublished - 30-09-2019

All Science Journal Classification (ASJC) codes

  • Modelling and Simulation
  • General Agricultural and Biological Sciences
  • Computational Mathematics
  • Applied Mathematics


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