An Improved Visibility Graph Analysis of EEG Signals of Alzheimer Brain

Wang, Ruofan; Yang, Zhongyou; Wang, Jiang; Shi, Lianshuan

doi:10.1109/cisp-bmei.2018.8633052

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…Table 1 lists the most common metrics based on our article review. For instance, Ji et al (2016) found that the degree distribution of the extracted brain network of individuals with job stress increases with the k coefficient, while that of normal individuals decreases with the k coefficient, or Wang et al (2018), by analyzing brain network features, showed that the connections in the brain network of individuals with Alzheimer's disease are more scattered than those of healthy individuals, indicating a scale-free property, and the range of connections in their brain network is reduced. Therefore, calculating and studying network analysis metrics in graph-theoretical area can be highly beneficial.…”

Section: Modularitymentioning

confidence: 99%

Visibility graph analysis for brain: scoping review

Sulaimany,

Safahi

2023

Front. Neurosci.

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In the past two decades, network-based analysis has garnered considerable attention for analyzing time series data across various fields. Time series data can be transformed into graphs or networks using different methods, with the visibility graph (VG) being a widely utilized approach. The VG holds extensive applications in comprehending, identifying, and predicting specific characteristics of time series data. Its practicality extends to domains such as medicine, economics, meteorology, tourism, and others. This research presents a scoping review of scholarly articles published in reputable English-language journals and conferences, focusing on VG-based analysis methods related to brain disorders. The aim is to provide a foundation for further and future research endeavors, beginning with an introduction to the VG and its various types. To achieve this, a systematic search and refinement of relevant articles were conducted in two prominent scientific databases: Google Scholar and Scopus. A total of 51 eligible articles were selected for a comprehensive analysis of the topic. These articles categorized based on publication year, type of VG used, rationale for utilization, machine learning algorithms employed, frequently occurring keywords, top authors and universities, evaluation metrics, applied network properties, and brain disorders examined, such as Epilepsy, Alzheimer’s disease, Autism, Alcoholism, Sleep disorders, Fatigue, Depression, and other related conditions. Moreover, there are recommendations for future advancements in research, which involve utilizing cutting-edge techniques like graph machine learning and deep learning. Additionally, the exploration of understudied medical conditions such as attention deficit hyperactivity disorder and Parkinson’s disease is also suggested.

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Section: Modularitymentioning

confidence: 99%

Visibility graph analysis for brain: scoping review

Sulaimany,

Safahi

2023

Front. Neurosci.

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“…In total, we used 12 features to classify the Alzheimer's (AD) and the robust normal elderly (RNE) groups. Six of these features have been tested in previous EEG graph theory studies of AD, namely clustering coefficient sequence similarity [15], average clustering coefficient [38,39], global efficiency [15,38,40], local efficiency [15,41], small-worldness [15] and graph index complexity [15,16]. The other six are graph features heavily studied in the field of computer science that, to the best of our knowledge, have not yet been considered in EEG graph theory studies.…”

Section: Feature Extractionmentioning

confidence: 99%

Machine Learning on Visibility Graph Features Discriminates the Cognitive Event-Related Potentials of Patients with Early Alzheimer’s Disease from Healthy Aging

et al. 2023

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We present a framework for electroencephalography (EEG)-based classification between patients with Alzheimer’s Disease (AD) and robust normal elderly (RNE) via a graph theory approach using visibility graphs (VGs). This EEG VG approach is motivated by research that has demonstrated differences between patients with early stage AD and RNE using various features of EEG oscillations or cognitive event-related potentials (ERPs). In the present study, EEG signals recorded during a word repetition experiment were wavelet decomposed into 5 sub-bands (δ,θ,α,β,γ). The raw and band-specific signals were then converted to VGs for analysis. Twelve graph features were tested for differences between the AD and RNE groups, and t-tests employed for feature selection. The selected features were then tested for classification using traditional machine learning and deep learning algorithms, achieving a classification accuracy of 100% with linear and non-linear classifiers. We further demonstrated that the same features can be generalized to the classification of mild cognitive impairment (MCI) converters, i.e., prodromal AD, against RNE with a maximum accuracy of 92.5%. Code is released online to allow others to test and reuse this framework.

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“…31 The constructed graph inherits the time characteristics of the time series, including the regular graph representing the periodic sequence, the random graph representing the random sequence, and the scale-free network representing the fractal sequence. Previously, VG has been widely used in the study of AD electroencephalogram (EEG) signals, including the successful characterization of the abnormal topology of the AD complex network, 28,32 the effective diagnosis of AD patients, 33,34 the detection of the degree and state of brain fatigue, 35,36 and the successful characterization of the correlation of multiple time series. 37 This fully demonstrates that VG is a powerful network construction tool and a promising method for studying the dynamic characteristics of AD functional networks in time.…”

Section: Introductionmentioning

confidence: 99%

Intelligent algorithm for dynamic functional brain network complexity from CN to AD

Zhang

Cui

Lian

et al. 2021

Int J of Intelligent Sys

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Alzheimer's disease (AD) is the main cause of dementia in the elderly. To date, it remains largely unknown whether and how dynamic characteristics of the functional networks differ from cognitively normal (CN) to AD. Here, we propose an AD dynamic network complexity intelligent detecting algorithm based on visibility graph. The focal regions that caused the dynamic abnormality of the connection mode were intelligently detected by creating a dynamic complexity network on the basis of the dynamic functional network. The results showed that the brain areas with different dynamic complexity gradually shifted from the frontal lobe to the temporal lobe and the occipital lobe. This was

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An Improved Visibility Graph Analysis of EEG Signals of Alzheimer Brain

Cited by 6 publications

References 15 publications

Visibility graph analysis for brain: scoping review

Visibility graph analysis for brain: scoping review

Machine Learning on Visibility Graph Features Discriminates the Cognitive Event-Related Potentials of Patients with Early Alzheimer’s Disease from Healthy Aging

Intelligent algorithm for dynamic functional brain network complexity from CN to AD

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