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

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.

Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study

“…From this exploratory analysis, we chose the final method according to the highest classification performance achieved, in this case SlopEn. This performance has been recently confirmed in another classification study [ 21 ].…”

“…Further characterisation of the classification performance using SlopEn was carried out using the Matthews Correlation Coefficient (MCC) [ 38 ], a more robust metric when classes are unbalanced. MCC ranges from −1 to 1, with 1 for a perfect classification, 0.5 for 75% correct prediction, and 0 for a random guess [ 21 ].…”

Double Feature Extraction Method of Ship-Radiated Noise Signal Based on Slope Entropy and Permutation Entropy

“…In recent years, SlEn has been further developed and applied. In September 2020, David Cuesta-Frau et al [ 37 ] proposed a method based on SlEn for distinguishing differences in body temperature records deriving from various classes of disease backgrounds, and their conclusions indicate that SlEn has a high ability to discriminate the temperature record sequences of different patients with dengue, malaria, a malignant tumor, and leptospirosis. In November 2020, David Cuesta-Frau et al [ 38 ] designed a study based on SlEn to compare dynamic recordings from internal emotional outburst symptoms of long follow-up patients with bipolar disorder (BD), and the results proved that SlEn is practicable for easily distinguishing between depression and mania episodes.…”

A novel method for measuring sublingual temperature using conventional non-contact forehead thermometer

“…Subjects were patients who were enrolled in an ongoing study for the evaluation of 24 hr continuous tympanic temperature in undifferentiated fever. 7 Subjects were approached by the primary researcher and informed consent was obtained prior to enrollment into the study. No financial assistance was provided to the participants for enrollment into the study.…”