Electroencephalography‐based feature extraction using complex network for automated epileptic seizure detection

Artameeyanant, Patcharin; Sultornsanee, Sivarit; Chamnongthai, Kosin

doi:10.1111/exsy.12211

Cited by 12 publications

(5 citation statements)

References 45 publications

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“…Network analysis metrics can be defined based on different network features, including connectivity, centrality, and distance (Artameeyanant et al, 2017)…”

Section: Visibility Graph Analysismentioning

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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“…Network analysis metrics can be defined based on different network features, including connectivity, centrality, and distance (Artameeyanant et al, 2017)…”

Section: Visibility Graph Analysismentioning

confidence: 99%

Visibility graph analysis for brain: scoping review

Sulaimany,

Safahi

2023

Front. Neurosci.

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“…This technique is used to test the influence of the independent variable on the dependent variable. ANOVA uses the F-Ratio test that allows multiple groups data through that variance between groups and within groups is determined (Artameeyanant et al, 2017).…”

Section: Analysis Of Variancementioning

confidence: 99%

Computer‐aided diagnosis ofCOVID‐19 disease from chest X‐ray images integrating deep feature extraction

et al. 2021

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The novel coronavirus (COVID-19) has an enormous impact on the daily lives and health of people residing in more than 200 nations. This article proposes a deep learning-based system for the rapid diagnosis of COVID-19. Chest x-ray radiograph images were used because recent findings revealed that these images contain salient features about COVID-19 disease. Transfer learning was performed using different pre-trained convolutional neural networks models for binary (normal and COVID-19) and triple (normal, COVID-19 and viral pneumonia) class problems. Deep features were extracted from a fully connected layer of the ResNET50v2 model and feature dimension was reduced through feature reduction methods. Feature fusion of feature sets reduced through analysis of variance (ANOVA) and mutual information feature selection (MIFS) was fed to Fine K-nearest neighbour to perform binary classification. Similarly, serial feature fusion of MIFS and chi-square features were utilized to train Medium Gaussian Support Vector Machines to distinguish normal, COVID-19 and viral pneumonia cases. The proposed framework yielded accuracies of 99.5% for binary and 95.5% for triple class experiments. The proposed model shows better performance than the existing methods, and this research has the potential to assist medical professionals to enhance the diagnostic ability to detect coronavirus disease.

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“…For instance, Zhang et al (2022) used the weighted adjacency matrix as a feature representation for classifying different sleep stages using calcium imaging data. In contrast, Mohammadpoory et al (2023) experimented with various methods to extract features from adjacency matrices such as Graph Index Complexity (GIC), Characteristic Path Length (CPL), Global Efficiency (GE) ( Latora and Marchiori, 2001 ), Local Efficiency (LE) ( Latora and Marchiori, 2001 ), Clustering Coefficients (CC) ( Saramäki et al, 2007 ), and Assortative Coefficient (AC) ( Artameeyanant et al, 2017 ). Supriya et al (2016) took a different approach and calculated two network properties: modularity ( Blondel, 2008 ) and an average weighted degree ( Antoniou and Tsompa, 2008 ) from the graph.…”

Section: Introductionmentioning

confidence: 99%

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

Patel,

Smith,

Yildirim

2024

Front. Neuroinform.

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Recently, graph theory has become a promising tool for biomedical signal analysis, wherein the signals are transformed into a graph network and represented as either adjacency or Laplacian matrices. However, as the size of the time series increases, the dimensions of transformed matrices also expand, leading to a significant rise in computational demand for analysis. Therefore, there is a critical need for efficient feature extraction methods demanding low computational time. This paper introduces a new feature extraction technique based on the Gershgorin Circle theorem applied to biomedical signals, termed Gershgorin Circle Feature Extraction (GCFE). The study makes use of two publicly available datasets: one including synthetic neural recordings, and the other consisting of EEG seizure data. In addition, the efficacy of GCFE is compared with two distinct visibility graphs and tested against seven other feature extraction methods. In the GCFE method, the features are extracted from a special modified weighted Laplacian matrix from the visibility graphs. This method was applied to classify three different types of neural spikes from one dataset, and to distinguish between seizure and non-seizure events in another. The application of GCFE resulted in superior performance when compared to seven other algorithms, achieving a positive average accuracy difference of 2.67% across all experimental datasets. This indicates that GCFE consistently outperformed the other methods in terms of accuracy. Furthermore, the GCFE method was more computationally-efficient than the other feature extraction techniques. The GCFE method can also be employed in real-time biomedical signal classification where the visibility graphs are utilized such as EKG signal classification.

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Electroencephalography‐based feature extraction using complex network for automated epileptic seizure detection

Cited by 12 publications

References 45 publications

Visibility graph analysis for brain: scoping review

Visibility graph analysis for brain: scoping review

Computer‐aided diagnosis ofCOVID‐19 disease from chest X‐ray images integrating deep feature extraction

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

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