ArticleEntropy (Basel, Switzerland)2018
Model Selection for Body Temperature Signal Classification Using Both Amplitude and Ordinality-Based Entropy Measures.
Article in Entropy (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Slope Entropy Normalisation by Means of Analytical and Heuristic Reference Values.Entropy (Basel, Switzerland) · 2022Article
- Pressure Injury Link to Entropy of Abdominal Temperature.Entropy (Basel, Switzerland) · 2022Article
- Classification of Actigraphy Records from Bipolar Disorder Patients Using Slope Entropy: A Feasibility Study.Entropy (Basel, Switzerland) · 2020Article
- Fever Time Series Analysis Using Slope Entropy. Application to Early Unobtrusive Differential Diagnosis.Entropy (Basel, Switzerland) · 2020Article
- Using the Information Provided by Forbidden Ordinal Patterns in Permutation Entropy to Reinforce Time Series Discrimination Capabilities.Entropy (Basel, Switzerland) · 2020Article
- Rope Tension Fault Diagnosis in Hoisting Systems Based on Vibration Signals Using EEMD, Improved Permutation Entropy, and PSO-SVM.Entropy (Basel, Switzerland) · 2020Article
- Embedded Dimension and Time Series Length. Practical Influence on Permutation Entropy and Its Applications.Entropy (Basel, Switzerland) · 2019Article
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Authors and funding
7 authors.
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Abstract
Many entropy-related methods for signal classification have been proposed and exploited successfully in the last several decades. However, it is sometimes difficult to find the optimal measure and the optimal parameter configuration for a specific purpose or context. Suboptimal settings may therefore produce subpar results and not even reach the desired level of significance. In order to increase the signal classification accuracy in these suboptimal situations, this paper proposes statistical models created with uncorrelated measures that exploit the possible synergies between them. The methods employed are permutation entropy (PE), approximate entropy (ApEn), and sample entropy (SampEn). Since PE is based on subpattern ordinal differences, whereas ApEn and SampEn are based on subpattern amplitude differences, we hypothesized that a combination of PE with another method would enhance the individual performance of any of them. The dataset was composed of body temperature records, for which we did not obtain a classification accuracy above 80% with a single measure, in this study or even in previous studies. The results confirmed that the classification accuracy rose up to 90% when combining PE and ApEn with a logistic model.
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