Information theory reveals physiological manifestations of COVID-19 that correlate with symptom density of illness.

Algorithms for the detection of COVID-19 illness from wearable sensor devices tend to implicitly treat the disease as causing a stereotyped (and therefore recognizable) deviation from healthy physiology. In contrast, a substantial diversity of bodily responses to SARS-CoV-2 infection have been reported in the clinical milieu. This raises the question of how to characterize the diversity of illness manifestations, and whether such characterization could reveal meaningful relationships across different illness manifestations. Here, we present a framework motivated by information theory to generate quantified maps of illness presentation, which we term "manifestations," as resolved by continuous physiological data from a wearable device (Oura Ring). We test this framework on five physiological data streams (heart rate, heart rate variability, respiratory rate, metabolic activity, and sleep temperature) assessed at the time of reported illness onset in a previously reported COVID-19-positive cohort (N = 73). We find that the number of distinct manifestations are few in this cohort, compared to the space of all possible manifestations. In addition, manifestation frequency correlates with the rough number of symptoms reported by a given individual, over a several-day period prior to their imputed onset of illness. These findings suggest that information-theoretic approaches can be used to sort COVID-19 illness manifestations into types with real-world value. This proof of concept supports the use of information-theoretic approaches to map illness manifestations from continuous physiological data. Such approaches could likely inform algorithm design and real-time treatment decisions if developed on large, diverse samples.

A Machine Learning Approach to COVID-19 Detection via Graphene Field-Effect-Transistor (GFET).

In the wake of the COVID-19 pandemic, there has been a need for reliable diagnostic testing. However, state-of-the-art detection methods rely on laboratory tests and also vary in accuracy. We evaluate that the usage of a graphene field-effect-transistor (GFET) coupled with machine learning can be a promising alternate diagnostic testing method. We processed the current-voltage data gathered from the GFET sensors to assess information about the presence of COVID-19 in biosamples. We perform binary classification using the following machine learning algorithms: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) with the Radial Basis Function (RBF) kernel, and K-Nearest Neighbors (KNN) in conjunction with Principal Component Analysis (PCA). We find that LDA and SVM with RBF proved to be the most accurate in identifying positive and negative samples, with accuracies of 99% and 98.5%, respectively. Based on these results, there is promise to develop a bioelectronic diagnostic method for COVID-19 detection by combining GFET technology with machine learning.