Multi-scale AM–FM analysis for the classification of surface electromyographic signals

Christodoulou, C.I.; Kaplanis, P. A.; Murray, Víctor; Pattichis, Marios S.; Pattichis, Constantinos S.; Kyriakides, Theodoros

doi:10.1016/j.bspc.2012.01.001

Cited by 19 publications

(13 citation statements)

References 11 publications

(29 reference statements)

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“…In the following sections a review of all the literature studies based on SVMs to implement HCI systems is provided. The focus is mainly on HCI used for controlling purposes, thus disregarding, for example, papers using SVM to detect neuromuscular disorders [132,133], to discriminate contaminated from clean EMG [134], to discriminate efforts stages in prolonged running [135]. Papers are divided according to the body segment involved and/or the tasks to be recognized: hand movements, finger movements, arm movements, walking modes, facial and whole body movements.…”

Section: Emg-based Hci and Svm 41 Overview Of Emg-based Hci Systemsmentioning

confidence: 99%

Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review

et al. 2017

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Support vector machines (SVMs) are widely used classifiers for detecting physiological patterns in human-computer interaction (HCI). Their success is due to their versatility, robustness and large availability of free dedicated toolboxes. Frequently in the literature, insufficient details about the SVM implementation and/or parameters selection are reported, making it impossible to reproduce study analysis and results. In order to perform an optimized classification and report a proper description of the results, it is necessary to have a comprehensive critical overview of the applications of SVM. The aim of this paper is to provide a review of the usage of SVM in the determination of brain and muscle patterns for HCI, by focusing on electroencephalography (EEG) and electromyography (EMG) techniques. In particular, an overview of the basic principles of SVM theory is outlined, together with a description of several relevant literature implementations. Furthermore, details concerning reviewed papers are listed in tables and statistics of SVM use in the literature are presented. Suitability of SVM for HCI is discussed and critical comparisons with other classifiers are reported.

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Section: Emg-based Hci and Svm 41 Overview Of Emg-based Hci Systemsmentioning

confidence: 99%

Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review

et al. 2017

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“…Return to step 4 until all filterbanks have been considered Select optimal filterbank configuration 11 Select the filterbank that gave optimal performance 12 Implement AM-FM system with optimal filterbank Figure 1. Adaptive multiscale AM -FM framework for texture analysis…”

Section: Adaptive Multiscale Am-fm Texture Analysis Systemmentioning

confidence: 99%

“…Multiscale AM-FM methods were applied to chest radiographs [9], ultrasound images of the carotid artery [10], classification of surface electromyographic signals [11], and retinal image classification [1].…”

Section: Introductionmentioning

confidence: 99%

An adaptive multiscale AM-FM texture analysis system with application to hysteroscopy imaging

Constantinou

Pattichis

Tanos

et al. 2012

2012 IEEE 12th International Conference on Bioinformatics &Amp; Bioengineering (BIBE)

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The use of multiscale AM-FM analysis systems has been recently demonstrated in a variety of applications in medical image analysis. In all of these applications, a fixed filter-bank is used as a preprocessing step for estimating different AM-FM components from different scales. In this paper, for the first time, we introduce the use of an adaptive, multiscale AM-FM approach that searches for the optimal filter-bank specification for use in image classification. We demonstrate an example application in hysteroscopy imaging, for identification of gynaecological cancer, where the optimal filter-bank turns out to be circularly symmetric.

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“…Multiscale based features estimated at various scales like instantaneous amplitude, instantaneous frequency and instantaneous phase have been used as input to K-nearest neighbor (KNN), self-organizing map (SOM) and support vector machine (SVM). These classifiers are used for classification of normal and abnormal EMG signals [4]. A combination of mutual information based feature combined with SVM technique has been developed to classify EMG signal [5].…”

Section: Introductionmentioning

confidence: 99%

Discrimination of Myopathy, Neuropathy and Healthy EMG Signals

Singh¹,

Agrawal²,

Gupta³

2017

International Journal of Advanced Research in Computer and Comm

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Abstract:In this paper, method for discrimination of EMG signals is proposed. EMG signal is decomposed into set of narrow band signal using empirical mode decomposition (EMD) process known as intrinsic mode functions (IMFs). The features namely mean, standard deviation, variance and Entropy of the intrinsic mode functions (IMFs) generated by EMD process is used to discrimination of myopathy, neuropathy, and healthyEMG signals. The statistical features of IMFs have provided better discrimination performance.

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Multi-scale AM–FM analysis for the classification of surface electromyographic signals

Cited by 19 publications

References 11 publications

Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review

Support vector machines to detect physiological patterns for EEG and EMG-based human–computer interaction: a review

An adaptive multiscale AM-FM texture analysis system with application to hysteroscopy imaging

Discrimination of Myopathy, Neuropathy and Healthy EMG Signals

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