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 ].…”

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.…”

Particle Swarm Optimization Fractional Slope Entropy: A New Time Series Complexity Indicator for Bearing Fault Diagnosis

“…Since it was proposed, it has been used in the fields of medical, hydroacoustics, and fault diagnosis. In 2019, SlEn was first proposed by David Cuesta-Frau, and successively applied it to the classification of electroencephalographic (EEG) records and electromyogram (EMG) records [27], classify the activity records of patients with bipolar disorder [28], and the features extraction of fever time series [29]. Then SlEn is also used to extract the features of ship radiated noise signals [30] and bearing fault signals [31].…”