Abstract
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.
Highlights
The current Covid-19 pandemic has demonstrated once more how important it is to closely monitor body temperature at a very large scale, remotely, and continuously
As [17] employed Sample Entropy (SampEn) as the discriminating feature, when Slope Entropy (SlopEn) was not available yet, and since SlopEn seemed very promising as it outperformed SampEn in many comparative analyzes [22], it was necessary to assess the capability of this new method in the framework of temperature time series
SlopEn can be a promising tool for assessing differences among fever patterns in body temperature time series
Summary
The current Covid-19 pandemic has demonstrated once more how important it is to closely monitor body temperature at a very large scale, remotely, and continuously. The infra-red clinical temperature measuring technology is very convenient in terms of non-obtrusiveness and lack of contact (distance), in its current form it is certainly not more than a plain fever/no fever assessment tool [2]. It still overlooks the richer physiological information yielded by the output of the thermo-regulatory system that can be obtained from a temporal continuum perspective
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