ArticleSensors (Basel, Switzerland)2019
A Hybrid System for Distinguishing between Brain Death and Coma Using Diverse EEG Features.
Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Use of Multiple EEG Features and Artificial Neural Network to Monitor the Depth of Anesthesia.Sensors (Basel, Switzerland) · 2019Trial
- Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest.Sensors (Basel, Switzerland) · 2025Article
- Identifying Features of Electroencephalography Associated with Improved Awareness in Persistent Vegetative State via Multiscale Entropy: A Machine Learning Modeling Study.Neurotrauma reports · 2025Article
- Quantitative analysis and machine learning-based interpretation of EEG signals in coma and brain-death diagnosis.Cognitive neurodynamics · 2024Article
- Neural Dynamics Associated with Biological Variation in Normal Human Brain Regions.Entropy (Basel, Switzerland) · 2024Article
- Unconscious classification of quantitative electroencephalogram features from propofol versus propofol combined with etomidate anesthesia using one-dimensional convolutional neural network.Frontiers in medicine · 2024Article
- Quantitative Electroencephalography Analysis for Improved Assessment of Consciousness Levels in Deep Coma Patients Using a Proposed Stimulus Stage.Diagnostics (Basel, Switzerland) · 2023Article
- Electrophysiological and Neuroimaging Studies - During Resting State and Sensory Stimulation in Disorders of Consciousness: A Review.Frontiers in neuroscience · 2020Review
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Authors and funding
6 authors.
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Abstract
Electroencephalography (EEG) signals may provide abundant information reflecting the developmental changes in brain status. It usually takes a long time to finally judge whether a brain is dead, so an effective pre-test of brain states method is needed. In this paper, we present a hybrid processing pipeline to differentiate brain death and coma patients based on canonical correlation analysis (CCA) of power spectral density, complexity features, and feature fusion for group analysis. In addition, time-varying power spectrum and complexity were observed based on the analysis of individual patients, which can be used to monitor the change of brain status over time. Results showed three major differences between brain death and coma groups of EEG signal: slowing, increased complexity, and the improvement on classification accuracy with feature fusion. To the best of our knowledge, this is the first scheme for joint general analysis and time-varying state monitoring. Delta-band relative power spectrum density and permutation entropy could effectively be regarded as potential features of discrimination analysis on brain death and coma patients.
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