ArticleBrain informatics2014
Analysis of alcoholic EEG signals based on horizontal visibility graph entropy.
Article in Brain informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- EEG channel selection using metaheuristic algorithms in alcoholism detection using optimal wavelet transform.Scientific reports · 2026Article
- Finite mixtures of functional graphical models: Uncovering heterogeneous dependencies in high-dimensional data.PloS one · 2025Article
- Applications of Entropy in Data Analysis and Machine Learning: A Review.Entropy (Basel, Switzerland) · 2024Review
- Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms.Scientific reports · 2024Article
- Efficient novel network and index for alcoholism detection from EEGs.Health information science and systems · 2023Article
- Visibility graph analysis for brain: scoping review.Frontiers in neuroscience · 2023Article
- Deep Convolutional Neural Network Regularization for Alcoholism Detection Using EEG Signals.Sensors (Basel, Switzerland) · 2021Article
- Visibility graph based temporal community detection with applications in biological time series.Scientific reports · 2021Article
- The Entropy Universe.Entropy (Basel, Switzerland) · 2021Review
- Network Analysis of Time Series: Novel Approaches to Network Neuroscience.Frontiers in neuroscience · 2021Review
- On fractality of functional near-infrared spectroscopy signals: analysis and applications.Neurophotonics · 2020Article
- Wave2Vec: Vectorizing Electroencephalography Bio-Signal for Prediction of Brain Disease.International journal of environmental research and public health · 2018Article
- Decoding cortical brain states from widefield calcium imaging data using visibility graph.Biomedical optics express · 2018Article
- A review on EEG-based methods for screening and diagnosing alcohol use disorder.Cognitive neurodynamics · 2018Review
- An EEG-based machine learning method to screen alcohol use disorder.Cognitive neurodynamics · 2017Article
- Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series.Scientific reports · 2016Article
Corrections and comments
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Authors and funding
4 authors.
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Abstract
This paper proposes a novel horizontal visibility graph entropy (HVGE) approach to evaluate EEG signals from alcoholic subjects and controlled drinkers and compare with a sample entropy (SaE) method. Firstly, HVGEs and SaEs are extracted from 1,200 recordings of biomedical signals, respectively. A statistical analysis method is employed to choose the optimal channels to identify the abnormalities in alcoholics. Five group channels are selected and forwarded to a K-Nearest Neighbour (K-NN) and a support vector machine (SVM) to conduct classification, respectively. The experimental results show that the HVGEs associated with left hemisphere, [Formula: see text]1, [Formula: see text]3 and FC5 electrodes, of alcoholics are significantly abnormal. The accuracy of classification with 10-fold cross-validation is 87.5 [Formula: see text] with about three HVGE features. By using just optimal 13-dimension HVGE features, the accuracy is 95.8 [Formula: see text]. In contrast, SaE features associated cannot identify the left hemisphere disorder for alcoholism and the maximum classification ratio based on SaE is just 95.2 [Formula: see text] even using all channel signals. These results demonstrate that the HVGE method is a promising approach for alcoholism identification by EEG signals.
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