ArticleComputational and mathematical methods in medicine2013
Analyzing EEG of quasi-brain-death based on dynamic sample entropy measures.
Article in Computational and mathematical methods in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A comprehensive analysis of brain network complexity in task-based fMRI using entropy: systematic review.Brain imaging and behavior · 2026Pooled it
- Information-Theoretic Approaches in EEG Correlates of Auditory Perceptual Awareness under Informational Masking.Biology · 2023Article
- Multivariate Multiscale Cosine Similarity Entropy and Its Application to Examine Circularity Properties in Division Algebras.Entropy (Basel, Switzerland) · 2022Article
- Variational Embedding Multiscale Sample Entropy: A Tool for Complexity Analysis of Multichannel Systems.Entropy (Basel, Switzerland) · 2021Article
- EEG-based human emotion recognition using entropy as a feature extraction measure.Brain informatics · 2021Review
- Consciousness and complexity: a consilience of evidence.Neuroscience of consciousness · 2021Review
- A Hybrid System for Distinguishing between Brain Death and Coma Using Diverse EEG Features.Sensors (Basel, Switzerland) · 2019Article
- Multiscale Entropy of Electroencephalogram as a Potential Predictor for the Prognosis of Neonatal Seizures.PloS one · 2015Article
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3 authors.
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Abstract
To give a more definite criterion using electroencephalograph (EEG) approach on brain death determination is vital for both reducing the risks and preventing medical misdiagnosis. This paper presents several novel adaptive computable entropy methods based on approximate entropy (ApEn) and sample entropy (SampEn) to monitor the varying symptoms of patients and to determine the brain death. The proposed method is a dynamic extension of the standard ApEn and SampEn by introducing a shifted time window. The main advantages of the developed dynamic approximate entropy (DApEn) and dynamic sample entropy (DSampEn) are for real-time computation and practical use. Results from the analysis of 35 patients (63 recordings) show that the proposed methods can illustrate effectiveness and well performance in evaluating the brain consciousness states.
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