An EMG-based feature extraction method using a normalized weight vertical visibility algorithm for myopathy and neuropathy detection

Artameeyanant, Patcharin; Sultornsanee, Sivarit; Chamnongthai, Kosin

doi:10.1186/s40064-016-3772-2

Cited by 28 publications

(4 citation statements)

References 42 publications

(42 reference statements)

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“…textitClustering coefficient The clustering coefficient indicates the degree to which network nodes tend to cluster together (Gupta et al 2015). It measures the density of triangles in the graph, where a triangle is a set of three nodes mutually connected by edges (Artameeyanant et al 2016). The clustering coefficient is defined as Dorogovtsev and Mendes (2004):…”

Section: Basic Global Propertiesmentioning

confidence: 99%

A multilevel backbone extraction framework

Hmaida,

Cherifi,

El Hassouni

2024

Appl Netw Sci

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As networks grow in size and complexity, backbones become an essential network representation. Indeed, they provide a simplified yet informative overview of the underlying organization by retaining the most significant and structurally influential connections within a network. Network heterogeneity often results in complex and intricate structures, making it challenging to identify the backbone. In response, we introduce the Multilevel Backbone Extraction Framework, a novel approach that diverges from conventional backbone methodologies. This generic approach prioritizes the mesoscopic organization of networks. First, it splits the network into homogeneous-density components. Second, it extracts independent backbones for each component using any classical Backbone technique. Finally, the various backbones are combined. This strategy effectively addresses the heterogeneity observed in network groupings. Empirical investigations on real-world networks underscore the efficacy of the Multilevel Backbone approach in preserving essential network structures and properties. Experiments demonstrate its superiority over classical methods in handling network heterogeneity and enhancing network integrity. The framework is adaptable to various types of networks and backbone extraction techniques, making it a versatile tool for network analysis and backbone extraction across diverse network applications.

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Section: Basic Global Propertiesmentioning

confidence: 99%

A multilevel backbone extraction framework

Hmaida,

Cherifi,

El Hassouni

2024

Appl Netw Sci

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“…To normalize the amplitude and align the different phases, a fast weighted horizontal visibility algorithm (FWHVA) [24] is employed to convert each time series into a weighted horizontal visibility graph (WHVGs). WHVGs have been widely used in biomedical signal processing, such as EEG [28], [29], EMG [30] and Pulse wave [31]. This paper applies it to electromagnetic signal processing.…”

Section: Normalizing Electromagnetic Signals Using Graph Featuresmentioning

confidence: 99%

“…To normalize the amplitude and align the different phases, an oblique visibility graph (OVG) [24] is employed to convert those phase or amplitude sequence into a graph. OVG have been widely used in time series, such as EEG [28] and ECG [29]. This paper applies it to frequency domain processing by processing the amplitude and phase, respectively.. From a mathematical point of view, an amplitude or phase sequence of a signal can be regarded as a time series, X j (j = 1, .…”

Section: Normalizing Electromagnetic Signals Using Graph Featuresmentioning

confidence: 99%

Brain Injury Localization in Electromagnetic Imaging using Symmetric Crossing Lines Method

Zhu

Bialkowski

Crozier

et al. 2021

Preprint

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To avoid death or disability, patients with brain injury should undertake a diagnosis at the earliest time and accept frequent monitoring after starting any medical intervention. This paper presents a novel approach to localize brain injury using the intersection of pairs of signals from symmetrical antennas based on the hypothesis that healthy brains are approximately symmetric that the bleeding targets will lead to significantly different amplitude and phase changes if a pair of symmetrical transmit signals cross targets with the same distances. The scattered signals (S-parameters) across the band 0.5-2 GHz are acquired using 100 realistic brain models and 150 experimental data measurements. Each signal is divided into two frequency bands and then they are converted into two graphs. The neighbour pair signals from symmetric hemisphere are evaluated to detect and localize the potential abnormalities in the brain. The higher weighted pairs across the axes of symmetry are detected and used to localize any abnormal targets. The results indicate that crossing pairs of antenna signals from the hemisphere with a blood mass exhibit significantly different signal amplitude in the graph features compared to those without the target (p<0.003). The experiments show that our novel localization algorithm can achieve an accuracy of 0.85±0.08 Dice similarity coefficient based on 150 experimental measurements using an elliptical container, which is suitable for brain injury localization.

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“…In patients with neuropathy, the undamaged nerve transmits larger and more complex signals to the muscle fiber to compensate for damaged nerves. Therefore, EMG signals in patients with neuropathy are larger and more complex than those of normal subjects [3]. However, the physical characteristics, the location of the measurement, and the degree of activity of the muscles have a significant effect on the EMG signal measurement.…”

Section: Extended Abstractmentioning

confidence: 99%