Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring
| Autor: | I. R. Piper, T.Y.M. Lo, Javier Escudero, Evangelos Kafantaris |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Computer science
missing samples General Physics and Astronomy lcsh:Astrophysics 02 engineering and technology 030204 cardiovascular system & hematology computer.software_genre Symbolic data analysis Article 03 medical and health sciences 0302 clinical medicine Intensive care lcsh:QB460-466 nonlinear analysis 0202 electrical engineering electronic engineering information engineering Entropy (information theory) lcsh:Science Wearable technology business.industry 020206 networking & telecommunications lcsh:QC1-999 Improved performance symbolic data analysis Respiratory impedance outlier samples Outlier lcsh:Q dispersion entropy Data mining business computer lcsh:Physics Signal monitoring |
| Zdroj: | Entropy, Vol 22, Iss 3, p 319 (2020) Entropy Volume 22 Issue 3 Kafantaris, E, Piper, I, Lo, M & Escudero, J 2020, ' Augmentation of Dispersion Entropy for Handling Missing and Outlier Samples in Physiological Signal Monitoring ', Entropy, vol. 22, no. 3, 319 . https://doi.org/10.3390/e22030319 |
| ISSN: | 1099-4300 |
| DOI: | 10.3390/e22030319 |
| Popis: | Entropy quantification algorithms are becoming a prominent tool for the physiological monitoring of individuals through the effective measurement of irregularity in biological signals. However, to ensure their effective adaptation in monitoring applications, the performance of these algorithms needs to be robust when analysing time-series containing missing and outlier samples, which are common occurrence in physiological monitoring setups such as wearable devices and intensive care units. This paper focuses on augmenting Dispersion Entropy (DisEn) by introducing novel variations of the algorithm for improved performance in such applications. The original algorithm and its variations are tested under different experimental setups that are replicated across heart rate interval, electroencephalogram, and respiratory impedance time-series. Our results indicate that the algorithmic variations of DisEn achieve considerable improvements in performance while our analysis signifies that, in consensus with previous research, outlier samples can have a major impact in the performance of entropy quantification algorithms. Consequently, the presented variations can aid the implementation of DisEn to physiological monitoring applications through the mitigation of the disruptive effect of missing and outlier samples. |
| Databáze: | OpenAIRE |
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