A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

Chen, Maoqi; Zhou, Ping

doi:10.1109/tnsre.2015.2412038

Cited by 180 publications

(168 citation statements)

References 65 publications

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“…using advanced signal decomposition algorithms [23,24,[32][33][34]. However, not all of these algorithms are applicable to hdEMG data in pathological tremor; in pathological tremor, motor unit firings tend to be significantly more synchronized than in healthy conditions [21,23], causing bursts of EMG activity ( Figure 2) that are very difficult to decompose into the contributions of individual motor units.…”

Section: Neural Drive To Skeletal Muscles and Tremor Demonstration Inmentioning

confidence: 99%

New Perspectives for Computer-Aided Discrimination of Parkinson’s Disease and Essential Tremor

et al. 2017

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Pathological tremor is a common but highly complex movement disorder, affecting ∼5% of population older than 65 years. Different methodologies have been proposed for its quantification. Nevertheless, the discrimination between Parkinson's disease tremor and essential tremor remains a daunting clinical challenge, greatly impacting patient treatment and basic research. Here, we propose and compare several movement-based and electromyography-based tremor quantification metrics. For the latter, we identified individual motor unit discharge patterns from high-density surface electromyograms and characterized the neural drive to a single muscle and how it relates to other affected muscles in 27 Parkinson's disease and 27 essential tremor patients. We also computed several metrics from the literature. The most discriminative metrics were the symmetry of the neural drive to muscles, motor unit synchronization, and the mean log power of the tremor harmonics in movement recordings. Noteworthily, the first two most discriminative metrics were proposed in this study. We then used decision tree modelling to find the most discriminative combinations of individual metrics, which increased the accuracy of tremor type discrimination to 94%. In summary, the proposed neural drive-based metrics were the most accurate at discriminating and characterizing the two most common pathological tremor types.

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Section: Neural Drive To Skeletal Muscles and Tremor Demonstration Inmentioning

confidence: 99%

New Perspectives for Computer-Aided Discrimination of Parkinson’s Disease and Essential Tremor

et al. 2017

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“…The second approach for sEMG decomposition uses blind-source separation (BSS) techniques to identify trains of recurring MUAPs from the statistical properties of HDsEMG signals (73)(74)(75). The convolution kernel compensation (CKC) algorithm of Holobar and Zazula (74, 77) has been reported to be able to decompose as many as 30 MUAP trains from an HDsEMG signal (23).…”

Section: Semg Decompositionmentioning

confidence: 99%

Electromyographic (EMG) Decomposition

Marateb

McGill

2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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An electromyographic (EMG) signal is a biomedical signal derived from measuring electrical potentials generated in contracting muscles. EMG signals can be recorded using either intramuscular (iEMG) or surface electrodes (sEMG). EMG signals are composed of motor unit action potential (MUAP) trains, the MUAP being the signal detected in response to the discharge of a group of muscle fibers innervated by a single motoneuron. The process of extracting the MUAP trains from an EMG signal is referred to as EMG decomposition. EMG decomposition makes it possible to study the behavior of individual motoneurons and thus to decode the neural drive to a muscle at a very precise level. This article reviews the different electrodes used to record iEMG and sEMG signals, the main steps in pattern‐matching decomposition algorithms (filtering, segmentation, clustering, classification, and resolving superpositions), the blind‐source separation techniques used for decomposing high‐density sEMG signals, the principal decomposition programs that have been described in the literature, including those that are publicly available, and approaches for assessing the accuracy of decomposition algorithms and particular decomposition results. Finally, an interactive tutorial on iEMG decomposition is provided.

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“…Bell-Sejnowski algorithm [15], extended ICA [16], and JADE [14]. In our implementation, the fast ICA algorithm [17] was applied to extract Ψ 3 from Y due to its high efficiency.…”

Section: ) Principal Component Analysismentioning

confidence: 99%

Blind source separation and artefact cancellation for single channel bioelectrical signal

Zhang

Mandic

2016

2016 IEEE 13th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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Abstract-Bioelectrical signal analysis is gaining significant interests from both academics and industries due to its capability for improved diagnosis and therapy of chronic diseases. In practice, different bio-signals, such as EEG, ECG, EOG and EMG, are usually contaminating each other, and the measured signal is the linear combination of them. It is critical to separate them since analysis of one type or several of them separately is of more interest. In the case of multichannel recording, several blind source separation methods are available to extract its original components. However, for single channel scenarios, the problem has yet to be well studied. Therefore in this paper, we explore blind source separation and artefact cancellation for a single channel signal by combining signal decomposition method singular spectrum analysis (SSA) with different blind source separation methods, such as principal component analysis (PCA), maximum noise fraction (MNF), independent component analysis (ICA) and canonical correlation analysis (CCA). We also systematically compare the separation performance by combing different decomposition methods (wavelet transform (WT), ensemble empirical mode decomposition (EEMD) and SSA) with blind source separation methods (PCA, MNF ICA and CCA). The good simulation results have demonstrated the effectiveness and efficiency of the proposed method.

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A Novel Framework Based on FastICA for High Density Surface EMG Decomposition

Cited by 180 publications

References 65 publications

New Perspectives for Computer-Aided Discrimination of Parkinson’s Disease and Essential Tremor

New Perspectives for Computer-Aided Discrimination of Parkinson’s Disease and Essential Tremor

Electromyographic (EMG) Decomposition

Blind source separation and artefact cancellation for single channel bioelectrical signal

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