FastICA peel-off for ECG interference removal from surface EMG

Chen, Maoqi; Zhang, Xu; Chen, Xiang; Zhu, Mingxing; Li, Guanglin; Zhou, Ping

doi:10.1186/s12938-016-0196-8

Cited by 16 publications

(8 citation statements)

References 15 publications

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“…Nonetheless, the effects of motion artifacts can be reduced by proper design of the electronic circuitry and set-up, but not eliminated [ 21 ]. The ECG interference is difficult to remove with conventional filters because the contamination overlaps with the sEMG signal in both the time domain and frequency domain [ 22 ]. For these reasons, it is essential to design classification systems that are robust enough to operate on signals containing such artifacts or can detect these artifacts so that signals containing them are discarded.…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Moura

Balbinot

2018

Sensors

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A few prosthetic control systems in the scientific literature obtain pattern recognition algorithms adapted to changes that occur in the myoelectric signal over time and, frequently, such systems are not natural and intuitive. These are some of the several challenges for myoelectric prostheses for everyday use. The concept of the virtual sensor, which has as its fundamental objective to estimate unavailable measures based on other available measures, is being used in other fields of research. The virtual sensor technique applied to surface electromyography can help to minimize these problems, typically related to the degradation of the myoelectric signal that usually leads to a decrease in the classification accuracy of the movements characterized by computational intelligent systems. This paper presents a virtual sensor in a new extensive fault-tolerant classification system to maintain the classification accuracy after the occurrence of the following contaminants: ECG interference, electrode displacement, movement artifacts, power line interference, and saturation. The Time-Varying Autoregressive Moving Average (TVARMA) and Time-Varying Kalman filter (TVK) models are compared to define the most robust model for the virtual sensor. Results of movement classification were presented comparing the usual classification techniques with the method of the degraded signal replacement and classifier retraining. The experimental results were evaluated for these five noise types in 16 surface electromyography (sEMG) channel degradation case studies. The proposed system without using classifier retraining techniques recovered of mean classification accuracy was of 4% to 38% for electrode displacement, movement artifacts, and saturation noise. The best mean classification considering all signal contaminants and channel combinations evaluated was the classification using the retraining method, replacing the degraded channel by the virtual sensor TVARMA model. This method recovered the classification accuracy after the degradations, reaching an average of 5.7% below the classification of the clean signal, that is the signal without the contaminants or the original signal. Moreover, the proposed intelligent technique minimizes the impact of the motion classification caused by signal contamination related to degrading events over time. There are improvements in the virtual sensor model and in the algorithm optimization that need further development to provide an increase the clinical application of myoelectric prostheses but already presents robust results to enable research with virtual sensors on biological signs with stochastic behavior.

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Section: Introductionmentioning

confidence: 99%

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Moura

Balbinot

2018

Sensors

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“…The performance of the algorithm was tested on 10 subjects only, possibly limiting the generalizability of the results. However, this sample size is comparable to or higher than those of previous studies in the field [5,14,16,28,29]. The analysis on real data, with the illustrative application of respiratory EMG, has to be considered as a feasibility analysis.…”

Section: Discussionmentioning

confidence: 93%

“…This is likely the case for additive noise, while other noise sources, such as multiplicative noise, can be interdependent with the desired signal. ICA was proposed for ECG denoising of trunk EMG under the assumption that EMG and ECG signals are independent [ 16 , 17 , 26 , 27 , 28 , 29 ]. In previous studies, the mixing matrix of the ICA algorithm was extracted by a synthetic dataset composed by combining the signals simultaneously recorded from multiple electrodes (from eight [ 27 ] to 16 [ 16 , 17 ] channels).…”

Section: Introductionmentioning

confidence: 99%

Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram

Peri

Ciccarelli

et al. 2021

Sensors

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A new algorithm based on singular value decomposition (SVD) to remove cardiac contamination from trunk electromyography (EMG) is proposed. Its performance is compared to currently available algorithms at different signal-to-noise ratios (SNRs). The algorithm is applied on individual channels. An experimental calibration curve to adjust the number of SVD components to the SNR (0–20 dB) is proposed. A synthetic dataset is generated by the combination of electrocardiography (ECG) and EMG to establish a ground truth reference for validation. The performance is compared with state-of-the-art algorithms: gating, high-pass filtering, template subtraction (TS), and independent component analysis (ICA). Its applicability on real data is investigated in an illustrative diaphragm EMG of a patient with sleep apnea. The SVD-based algorithm outperforms existing methods in reconstructing trunk EMG. It is superior to the others in the time (relative mean squared error < 15%) and frequency (shift in mean frequency < 1 Hz) domains. Its feasibility is proven on diaphragm EMG, which shows a better agreement with the respiratory cycle (correlation coefficient = 0.81, p-value < 0.01) compared with TS and ICA. Its application on real data is promising to non-obtrusively estimate respiratory effort for sleep-related breathing disorders. The algorithm is not limited to the need for additional reference ECG, increasing its applicability in clinical practice.

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“…removing the ECG artifact, can be processed through several different simple or more complex approaches [37]. High-pass frequency filters (HPF) [38], ECG subtraction through QRS-complex detection (FAS) [39], adaptive filtering (AF) approaches [40,41], ICA based approaches [42,43] and also combined AF-ICA based methods [44]. Here in this toolbox however, we promise to stick with the simple and fast methods and thus, implement a modified high-pass frequency filtering approach not only to gain from the simplicity but to also improve the performance accuracy of removing ECG artifact from EMG signals.…”

Section: Emg Quantificationmentioning

confidence: 99%

Essential Motor Cortex Signal Processing: an ERP and functional connectivity MATLAB toolbox -- user guide version 2.0

Seraj,

Mahalingam

2019

Preprint

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The purpose of this document is to help individuals use the "Essential Motor Cortex Signal Processing MATLAB Toolbox". The toolbox implements various methods for three major aspects of investigating human motor cortex from Neuroscience view point: (1) ERP estimation and quantification, (2) Cortical Functional Connectivity analysis and (3) EMG quantification. The toolbox -which is distributed under the terms of the GNU GENERAL PUBLIC LICENSE as a set of MATLAB R routines -can be downloaded directly at the address: http://oset.ir/category.php?dir=Tools .or from the public repository on GitHub, at address below:https://github.com/EsiSeraj/ERP Connectivity EMG Analysis . The purpose of this toolbox is threefold: 1. Extract the event-related-potential (ERP) from preprocessed cerebral signals (i.e. EEG, MEG, etc.), identify and then quantify the event-related synchronization/desynchronization (ERS/ERD) events. Both time-course dynamics and time-frequency (TF) analyzes are included. 2. Measure, quantify and demonstrate the cortical functional connectivity (CFC) across scalp electrodes. These set of functions can also be applied to various types of cerebral signals (i.e. electric and magnetic). 3. Quantify electromyogram (EMG) recorded from active muscles during performing motor tasks.

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FastICA peel-off for ECG interference removal from surface EMG

Cited by 16 publications

References 15 publications

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System

Singular Value Decomposition for Removal of Cardiac Interference from Trunk Electromyogram

Essential Motor Cortex Signal Processing: an ERP and functional connectivity MATLAB toolbox -- user guide version 2.0

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