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Abstract Most current algorithms for multivariate time series classification tend to overlook the correlations between time series of different variables. In this research, we propose a framework that leverages Eigen-entropy along with a cumulative moving window to derive time series signatures to support the classification task. These signatures are enumerations of correlations among different time series considering the temporal nature of the dataset. To manage dataset’s dynamic nature, we employ preprocessing with dense multi scale entropy. Consequently, the proposed framework, Eigen-entropy-based Time Series Signatures, captures correlations among multivariate time series without losing its temporal and dynamic aspects. The efficacy of our algorithm is assessed using six binary datasets sourced from the University of East Anglia, in addition to a publicly available gait dataset and an institutional sepsis dataset from the Mayo Clinic. We use recall as the evaluation metric to compare our approach against baseline algorithms, including dependent dynamic time warping with 1 nearest neighbor and multivariate multi-scale permutation entropy. Our method demonstrates superior performance in terms of recall for seven out of the eight datasets. 

Abstract Complex graphene electrode fabrication protocols including conventional chemical vapor deposition and graphene transfer techniques as well as more recent solution‐phase printing and postprint annealing methods have hindered the wide‐scale implementation of electrochemical devices including solid‐state ion‐selective electrodes (ISEs). Herein, a facile graphene ISE fabrication technique that utilizes laser induced graphene (LIG), formed by converting polyimide into graphene by a CO₂laser and functionalization with ammonium ion (NH₄⁺) and potassium ion (K⁺) ion‐selective membranes, is demonstrated. The electrochemical LIG ISEs exhibit a wide sensing range (0.1 × 10⁻³–150 × 10⁻³mfor NH₄⁺and 0.3 × 10⁻³–150 × 10⁻³mfor K⁺) with high stability (minimal drop in signal after 3 months of storage) across a wide pH range (3.5–9.0). The LIG ISEs are also able to monitor the concentrations of NH₄⁺and K⁺in urine samples (29–51% and 17–61% increase for the younger and older patient; respectively, after dehydration induction), which correlate well with conventional hydration status measurements. Hence, these results demonstrate a facile method to perform in‐field ion sensing and are the first steps in creating a protocol for quantifying hydration levels through urine testing in human subjects.