ArticlePloS one2016
A Comparison of Multiscale Permutation Entropy Measures in On-Line Depth of Anesthesia Monitoring.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Poincaré Plot Area of Gamma-Band EEG as a Measure of Emergence From Inhalational General Anesthesia.Frontiers in physiology · 2021Article
- Consciousness and complexity: a consilience of evidence.Neuroscience of consciousness · 2021Review
- Development and validation of a sample entropy-based method to identify complex patient-ventilator interactions during mechanical ventilation.Scientific reports · 2020Article
- Monitoring Depth of Anesthesia Based on Hybrid Features and Recurrent Neural Network.Frontiers in neuroscience · 2020Article
- Nonlinear dynamics captures brain states at different levels of consciousness in patients anesthetized with propofol.PloS one · 2019Article
- Data Decomposition Techniques with Multi-Scale Permutation Entropy Calculations for Bearing Fault Diagnosis.Sensors (Basel, Switzerland) · 2018Article
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6 authors.
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Abstract
objectiveMultiscale permutation entropy (MSPE) is becoming an interesting tool to explore neurophysiological mechanisms in recent years. In this study, six MSPE measures were proposed for on-line depth of anesthesia (DoA) monitoring to quantify the anesthetic effect on the real-time EEG recordings. The performance of these measures in describing the transient characters of simulated neural populations and clinical anesthesia EEG were evaluated and compared.
methodsSix MSPE algorithms-derived from Shannon permutation entropy (SPE), Renyi permutation entropy (RPE) and Tsallis permutation entropy (TPE) combined with the decomposition procedures of coarse-graining (CG) method and moving average (MA) analysis-were studied. A thalamo-cortical neural mass model (TCNMM) was used to generate noise-free EEG under anesthesia to quantitatively assess the robustness of each MSPE measure against noise. Then, the clinical anesthesia EEG recordings from 20 patients were analyzed with these measures. To validate their effectiveness, the ability of six measures were compared in terms of tracking the dynamical changes in EEG data and the performance in state discrimination. The Pearson correlation coefficient (R) was used to assess the relationship among MSPE measures.
resultsCG-based MSPEs failed in on-line DoA monitoring at multiscale analysis. In on-line EEG analysis, the MA-based MSPE measures at 5 decomposed scales could track the transient changes of EEG recordings and statistically distinguish the awake state, unconsciousness and recovery of consciousness (RoC) state significantly. Compared to single-scale SPE and RPE, MSPEs had better anti-noise ability and MA-RPE at scale 5 performed best in this aspect. MA-TPE outperformed other measures with faster tracking speed of the loss of unconsciousness.
conclusionsMA-based multiscale permutation entropies have the potential for on-line anesthesia EEG analysis with its simple computation and sensitivity to drug effect changes. CG-based multiscale permutation entropies may fail to describe the characteristics of EEG at high decomposition scales.
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