Augmenting Neuromuscular Disease Detection Using Optimally Parameterized Weighted Visibility Graph

Bose, Rohit; Samanta, Kaniska; Modak, Sudip; Chatterjee, Soumya

doi:10.1109/jbhi.2020.3001877

Cited by 10 publications

(5 citation statements)

References 35 publications

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“…Table 3 presents eight different feature extraction studies represented as F1 ( Mohammadpoory et al, 2023 ), F2 ( Javaid et al, 2022 ), F3 ( Supriya et al, 2016 ), F4 ( Hao et al, 2016 ), F5 ( Bose et al, 2020 ), F6 ( Cai et al, 2022 ), F7 ( Ahmadlou et al, 2010 ), and F8 (Proposed). In addition, it also illustrates the number of features extracted by each method per dataset.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

confidence: 99%

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

Patel,

Smith,

Yildirim

2024

Front. Neuroinform.

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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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“…Detail about the NB algorithm can be found in Reference 42. In previous literature, 16 NB classifier had been proved to be an efficient classification algorithm for EMG signal classification, and hence NB classifier is used in this present contribution.…”

Section: Naïve Bayesian (Nb)mentioning

confidence: 99%

“…Optimally parameterized weighted visibility graph (WVG) has been proved to be an effective tool for automatic diagnosis of neuromuscular diseases. 16 In joint time-frequency (T-F) analysis, ST is a popular technique that has been used to analyze nonstationary biomedical signals in exiting literature. 26,27 However, in the generalized version of ST, a fixed Gaussian window is used, which is not adaptive in nature.…”

Section: Introductionmentioning

confidence: 99%

Neuromuscular disease detection based on feature extraction from time–frequency images of EMG signals employing robust hyperbolic Stockwell transform

Samanta

Chatterjee

Bose

2022

Int J Imaging Syst Tech

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In this paper, a novel technique for detection of healthy (H), myopathy, (M) and amyotrophic lateral sclerosis (ALS) electromyography (EMG) signals is proposed employing robust hyperbolic Stockwell transform (HST). HST is an efficient signal processing technique to analyze any nonstationary signal in joint time-frequency (T-F) plane. However, a major issue with HST is the optimum selection of Gaussian window parameters since the resolution in the T-F plane depends on the shape of the window. Considering the aforesaid fact, in this article, a genetic algorithm (GA) based optimized HST is proposed for improved EMG signal analysis in T-F plane. Several novel features were extracted from HST spectrum and features with high statistical significance were selected for classification using several benchmark classifiers. It was observed that optimized HST resulted in better classification accuracy of EMG signals which indicates its potential for clinical applications.

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Augmenting Neuromuscular Disease Detection Using Optimally Parameterized Weighted Visibility Graph

Cited by 10 publications

References 35 publications

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

Gershgorin circle theorem-based feature extraction for biomedical signal analysis

A Review of Visibility Graph Analysis

Neuromuscular disease detection based on feature extraction from time–frequency images of EMG signals employing robust hyperbolic Stockwell transform

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