Detection of Healthy and Neuropathy Electromyograms Employing Stockwell Transform

Roy, Sayanjit Singha; Jana, Ramkrishna; Barman, Ritwick; Modak, Sudip; Halder, Preyashu; Bose, Rohit; Chatterjee, Soumya

doi:10.1109/aspcon.2018.8748438

Cited by 5 publications

(2 citation statements)

References 15 publications

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“… 136 Stockwell transform was applied to the EMG signal to extract features such as energy, mean, and standard deviation for the detection and classification of healthy and neuropathy. 137 Skewness and kurtosis of EEG spectra were utilized to detect mild cognitive impairment. 138 …”

Section: Resultsmentioning

confidence: 99%

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

et al. 2023

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Objective Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases. Methods A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection. Results The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance. Conclusions This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

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Section: Resultsmentioning

confidence: 99%

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

et al. 2023

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“…EMG signals are extensively used for the purpose of clinical diagnosis of several neuromuscular disorders, among which myopathy and Amyotrophic lateral sclerosis (ALS) are very common. Myopathy is a neuromuscular disorder that is caused due to the malfunctioning of fibre tissues connected to the skeletal muscles [1]. Symptoms of myopathy include muscle spasms or cramps during body movement, muscle fatigue etc.…”

Section: Introductionmentioning

confidence: 99%

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

Chatterjee

Roy

Bose³

et al. 2020

IET Science, Measurement & Technology

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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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Detection of Healthy and Neuropathy Electromyograms Employing Stockwell Transform

Cited by 5 publications

References 15 publications

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

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

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

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