Feature extraction from multifractal spectrum of electromyograms for diagnosis of neuromuscular disorders

Chatterjee, Soumya; Roy, Sayanjit Singha; Bose, Rohit; Pratiher, Sawon

doi:10.1049/iet-smt.2019.0132

Cited by 10 publications

(2 citation statements)

References 21 publications

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“…This roadmap presents the specific case of prosthetic control, nevertheless, the development of this technology could reveal useful to more applications where continuous monitoring is required. In clinical settings, continuous monitoring of EMG signals can be utilized to detect degenerative diseases of motorneurons [550] even for very large time spans such as weeks or months. In rehabilitation, EMG can be used as feedback to adapt the patient training accordingly to its muscular status, after a stroke or neurological impairments [551].…”

Section: Discussionmentioning

confidence: 99%

2022 roadmap on neuromorphic computing and engineering

Christensen

Dittmann

Linares-Barranco

et al. 2022

Neuromorph. Comput. Eng.

329

200

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Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.

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Section: Discussionmentioning

confidence: 99%

2022 roadmap on neuromorphic computing and engineering

Christensen

Dittmann

Linares-Barranco

et al. 2022

Neuromorph. Comput. Eng.

329

200

View full text Add to dashboard Cite

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“…The shortcomings of DFA are mono fractal components having variable characteristics and not having any information about its local fractal components. References [32,33] introduce the compensation for DFA problems using multifractal analysis with the help of multifractal detrended fluctuation analysis (MFDFA). It is a powerful and popular method.…”

Section: Introductionmentioning

confidence: 99%

A new protection scheme for PV-wind based DC-ring microgrid by using modified multifractal detrended fluctuation analysis

Anjaiah

Dash

2022

Prot Control Mod Power Syst

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This paper presents fault detection, classification, and location for a PV-Wind-based DC ring microgrid in the MATLAB/SIMULINK platform. Initially, DC fault signals are collected from local measurements to examine the outcomes of the proposed system. Accurate detection is carried out for all faults, (i.e., cable and arc faults) under two cases of fault resistance and distance variation, with the assistance of primary and secondary detection techniques, i.e. second-order differential current derivative $$\left( {\frac{{d^{2} I_{3} }}{{dt^{2} }}} \right)$$ d 2 I 3 d t 2 and sliding mode window-based Pearson’s correlation coefficient. For fault classification a novel approach using modified multifractal detrended fluctuation analysis (M-MFDFA) is presented. The advantage of this method is its ability to estimate the local trends of any order polynomial function with the help of polynomial and trigonometric functions. It also doesn’t require any signal processing algorithm for decomposition resulting and this results in a reduction of computational burden. The detected fault signals are directly passed through the M-MFDFA classifier for fault type classification. To enhance the performance of the proposed classifier, statistical data is obtained from the M-MFDFA feature vectors, and the obtained data is plotted in 2-D and 3-D scatter plots for better visualization. Accurate fault distance estimation is carried out for all types of faults in the DC ring bus microgrid with the assistance of recursive least squares with a forgetting factor (FF-RLS). To verify the performance and superiority of the proposed classifier, it is compared with existing classifiers in terms of features, classification accuracy (CA), and relative computational time (RCT).

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Electromyography signal classification using artificial intelligence

Subasi

2024

Applications of Artificial Intelligence in Healthcare and Biomedicine

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Feature extraction from multifractal spectrum of electromyograms for diagnosis of neuromuscular disorders

Cited by 10 publications

References 21 publications

2022 roadmap on neuromorphic computing and engineering

2022 roadmap on neuromorphic computing and engineering

A new protection scheme for PV-wind based DC-ring microgrid by using modified multifractal detrended fluctuation analysis

Electromyography signal classification using artificial intelligence

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