Detection of Focal EEG Signals Employing Weighted Visibility Graph

Modak, Sudip; Roy, Sayanjit Singha; Samanta, Kaniska; Chatterjee, Soumya; Dey, Sayantan; Bhowmik, Ronjoy; Bose, Rohit

doi:10.1109/iccece48148.2020.9223096

Cited by 9 publications

(3 citation statements)

References 20 publications

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“…Therefore, researchers must carefully consider their sample sizes to ensure the validity of their findings. Our investigations show the highest number of data samples in brain VG analysis were used for sleep-related studies (Xiong et al, 2019), 295, while the lowest number of data samples were used for epilepsy studies (Modak et al, 2020), 5. Likewise, the youngest participant in the sampling process was 1.5 years old, which was related to the epilepsy dataset.…”

Section: Figurementioning

confidence: 80%

“…For example, accuracy is often used to measure the degree of agreement between predicted and actual values, while ANOVA is used to analyze variance between groups. Other statistical metrics such as correlation coefficients, regression analysis, and hypothesis testing can also provide valuable insights into the relationships between 10.3389/fnins.2023.1268485 Ahmadlou et al, 2010;Cai et al, 2016;Wang et al, 2016bZhu et al, 2012Bhaduri and Ghosh, 2015;Hao et al, 2016a;Olamat et al, 2017Olamat et al, , 2018Olamat et al, , 2022Wang et al, 2017Wang et al, , 2020Xu et al, 2018;Mathur and Chakka, 2020;Tiwari and Wong, 2022;Villa Padilla et al, 2023 Bashiri andAhmadlou et al, 2013;Ozel et al, 2020Sengupta et al, 2014LPVG Cai et al, 2016Wang et al, 2016bGao et al, 2016Xu et al, 2018Pei et al, 2014Hao et al, 2016bGao et al, 2019WVG Cai et al, 2018Yu et al, 2020Wang et al, 2016aEbenezer Rajadurai and Valliyammai, 2018;Mathur and Chakka, 2020;Modak et al, 2020Samanta et al, 2019Supriya et al, 2018Sengupta et al, 2014Paranjape et al, 2023…”

Section: Figurementioning

confidence: 99%

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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: Figurementioning

confidence: 80%

Section: Figurementioning

confidence: 99%

Visibility graph analysis for brain: scoping review

Sulaimany,

Safahi

2023

Front. Neurosci.

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“…Moreover, we provide a comprehensive investigation of complex network criteria utilized in the context of visibility graph analysis in Table Ⅲ. [42]; [40]; [23] Generally, Table Ⅲ categorizes the most common metrics based on our article reviews. Besides, by analyzing visibility graphs in different fields of application, we can extract useful information about this network and characteristics of the data.…”

Section: E the Concept Of Visibility Graph Analysismentioning

confidence: 99%

A Review of Visibility Graph Analysis

Azizi,

Sulaimany

2024

IEEE Access

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A graph approach to time series data provides a new perspective for analyzing and comprehending the characteristics of the data. One common method of converting time series into a graph is through the use of visibility graph analysis. This technique has been developed and refined in various iterations over time. Moreover, visibility graph analysis has gained significant attention in recent years, with more than 88% of related articles being published since 2016. Its applications span across multiple fields, including medicine, economy, geology, weather, architecture, and industrial processes. Accordingly, to conduct this research, a comprehensive collection of all articles in this field was obtained by searching the title, abstract, and keywords from Science Direct and Google Scholar databases. Finally, 249 related articles were gathered. These articles were then analyzed based on their publication year, field of application, network type, journal source, university affiliation, and other relevant factors. Through this examination process, unexplored areas with potential for future exploration were identified. Specifically, the analysis of visibility graphs in security and service fields presents promising avenues for further investigation. Consequently, the study of visibility graph analysis offers valuable insights into time series data analysis. By understanding its applications across various disciplines and identifying untapped areas for research, we can expand our knowledge and contribute to advancements in this field.

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