ECG and PLI Artifacts Cancellation from Electromyogram

Kamboj, Promila; Singh, Simran

doi:10.9790/2834-0332227

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…ICA computes the matrix projection of one set of principal components onto another set of independent components so as to create the output variables that have maximized statistical independence on each others. In ICA, both the original sources and the mixture matrix are not known and therefore, this method is named as the Blind Separation of Sources (BSS) and is discussed in [6] [7] [11][12][13][14][15][16][17][18][19] .This can be done by calvulating the higher order statistical parameters of the signal by the use of various optimization techniques.…”

Section: Methodsmentioning

confidence: 99%

Performance of Various ICA Algorithms for an Electrocardiogram Signal

Subramanian*

2019

IJRTE

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De-noising of electrocardiogram signals is of utmost importance to diagnose the cardiovascular diseases. Many techniques have been utilized for filtering and removing noise from the raw electrocardiogram signal, such as traditional Wiener methods, neural networks and wavelet decomposition methods. These methods may lead to reduction in the amplitude of the QRS complex. The recorded multichannel electrocardiogram signal is correlated and is difficult for analysis. A suitable technique to overcome these problems is the appropriate use of Independent Component Analysis to maximize the required statistical parameters to make the output to be correlated. This work relates the performance of three of the Independent Component Analysis Algorithms such as JADE (Joint Approximate Diagonalisation of Eigen Matrices), Fixed Point ICA (Fast ICA) and AMUSE (Algorithm for Multiple Unknown signals Extraction) by which the three channel signal was made uncorrelated so that the best signal among the three different channels can be identified for further processing based on the values of the Signal to Interference Ratio(SIR) obtained in each of the algorithm .

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Section: Methodsmentioning

confidence: 99%

Performance of Various ICA Algorithms for an Electrocardiogram Signal

Subramanian*

2019

IJRTE

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“…The electrocardiogram signal (ECG) is considered a noise for myoelectric activity (EMG) and can be cancelled with a Butterworth filter, while another main noise that disturbs the EMG signal is the power line interference (PLI) which is removed with a notch filter. The performance evaluation of the suppression techniques can be done in terms of the signal-to-noise ratio or the mean square error [2].…”

Section: Introductionmentioning

confidence: 99%

Design Considerations for Improved Noise Rejection in Surface EMG Systems

Rubio¹,

Gutiérrez-Navarro²,

Trujillo³

2019

IJRTE

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Abstract: Bioelectric signals are distorted by unwanted electric noise interference. This paper focuses on techniques that can be applied to surface electromyographic systems design to improve the signal-to-noise ratio. Three case studies are presented in this manuscript : Effects of the front-end instrumentation amplifier gain, use of dc-dc converters for single-supply operation, and dedicated hardware for 60 Hz power line noise rejection. Results show that the quality of the signal is highly improved when the suggested techniques are applied.

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Various Denoising Techniques Utilization For Qualitative Analysis of ECG Signal: Towards the Proper Diagnosis of Cardiovascular Disease

Ahmad

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Electrocardiogram (ECG) is an extremely powerful method for cardiovascular disease identification. Nevertheless, the ECG data is corrupted during the recording of ECG signals by several forms of noises as for example, power lines interference, base lines wandering, electrode movement, muscle movement (EMG) etc. Such noises / artifacts confuse the proper diagnosis of heart ailments and therefore their removal is much needed. Up to some degree traditional filters exclude the artifacts, but these filters are static and cannot adjust their coefficients to environmental change. Adaptive filtering algorithm and EMD are also utilized to exclude artifacts from the ECG signals. To decompose a signal whose IMFs represents the mean of a set of measurements, each consisting of a signal plus a white finite amplitudinal noise.

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ECG and PLI Artifacts Cancellation from Electromyogram

Cited by 3 publications

References 16 publications

Performance of Various ICA Algorithms for an Electrocardiogram Signal

Performance of Various ICA Algorithms for an Electrocardiogram Signal

Design Considerations for Improved Noise Rejection in Surface EMG Systems

Various Denoising Techniques Utilization For Qualitative Analysis of ECG Signal: Towards the Proper Diagnosis of Cardiovascular Disease

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