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Patterns of Fever01:26

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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
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Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
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Classification of fever patterns using a single extracted entropy feature: A feasibility study based on Sample

David Cuesta-Frau1,2, Pau Miró-Martínez3, Sandra Oltra-Crespo1

  • 1Technological Institute of Informatics(ITI), Universitat Politècnica de València, Campus Alcoi, Plaza Ferrándiz y Carbonell, 2, 03801, Alcoi, Spain.

Mathematical Biosciences and Engineering : MBE
|November 17, 2019
PubMed
Summary

This study shows that a single mathematical feature, Sample Entropy, can help classify diseases based on fever patterns. This approach achieved nearly 70% accuracy in differentiating infectious diseases, tuberculosis, and dengue fever.

Keywords:
Sample entropyTrace segmentationdenguediagnostic aidsfevertime series classificationtuberculosis

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Area of Science:

  • Medical Informatics
  • Biophysics
  • Clinical Medicine

Background:

  • Fever patterns vary with disease pathology.
  • Previous studies used multiple features for disease classification.
  • Classifying diseases using a single mathematical feature remains unexplored.

Purpose of the Study:

  • To assess the feasibility of classifying diseases using a single mathematical feature: Sample Entropy.
  • To analyze fever temporal patterns for disease differentiation.

Main Methods:

  • Observational study with 103 patients.
  • Collected 24-hour continuous tympanic temperature data.
  • Extracted Sample Entropy feature from temperature data.

Main Results:

  • Achieved an overall classification accuracy close to 70%.
  • Demonstrated significant differences across disease classes (infectious, tuberculosis, non-tuberculosis, dengue fever).
  • Confirmed the feasibility of using Sample Entropy for disease classification.

Conclusions:

  • Sample Entropy is a viable single mathematical feature for classifying diseases based on fever patterns.
  • This method offers a novel approach to disease differentiation using physiological data.