Filtering and analyzing normal and abnormal electromyogram signals

Elouaham, Samir; Dliou, Azzedine; Laaboubi, Mostafa; Latif, Rachid; Elkamoun, Najib; Zougagh, Hicham

doi:10.11591/ijeecs.v20.i1.pp176-184

Cited by 9 publications

(15 citation statements)

References 17 publications

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“…that extract the j-th IMF decomposed by EMD. wi is the white noise [9,10,11]. The CEEMDAN algorithm is described as follows:…”

Section: Complete Ensemble Empirical Mode Decomposition With Adaptivementioning

confidence: 99%

“…In this paper, the methods as Empirical Mode Decomposition EMD and its recent versions Ensemble EMD (EEMD) and completed EEMD with adaptive noise (CEEMDAN) and the new version of these techniques have been proposed. These methods are tested in various areas as geophysical logging data, rotating machinery, changing climate and biomedical signals [5,6,7,8,9,10,11,12]. The EMD technique is a suitable tool for decomposing the non-stationary signal.…”

Section: Introductionmentioning

confidence: 99%

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Analysis Electroencephalogram Signals Using Denoising and Time-Frequency Techniques

2021

IJATCSE

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The electroencephalogram (EEG) is a test that determines brain activity. The existence of artifacts in EEG can naturally decrease the smoothness of the analysis of the biomedical signal. EEG disturbed by noises during encephalogram recordings is one of the problems that the experts have to investigate for finding solutions to remove these artifacts. To obtain an accurate EEG signal; the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is used and compared with and its old versions. The non-stationarity and non-linearity of the EEG signal cannot provide complete information when using the traditional method (Temporal and frequency domains). Among the objectives is the comparative analysis of time-frequency distributions applied on EEG signals. The denoising and time-frequency methods used in this study are tested on healthy and abnormal EEG signals which are disturbed by natural and artificial noises. The comparative study in this work shows the effectiveness of the combination of the ICEEMDAN and Periodogram methods that are suitable for denoising and analyzing the EEG signal.

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“…that extract the j-th IMF decomposed by EMD. wi is the white noise [9,10,11]. The CEEMDAN algorithm is described as follows:…”

Section: Complete Ensemble Empirical Mode Decomposition With Adaptivementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis Electroencephalogram Signals Using Denoising and Time-Frequency Techniques

2021

IJATCSE

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“…MUAP is the wave that contains information about muscular dystrophy and neuropathies, nerve conduction, and neuropathies [3]- [5]. The electromyogram and electroencephalogram signals are non-linear and non-stationary [6], [7]. Due to the latter, denoising and analyzing these signals are very important.…”

Section: Introductionmentioning

confidence: 99%

Denoising electromyogram and electroencephalogram signals using improved complete ensemble empirical mode decomposition with adaptive noise

Elouaham

Dliou

Elkamoun

et al. 2021

IJEECS

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The health of the brain and muscles depends on the proper analysis of electroencephalogram and electromyogram signals without noise. The latter blends into the recording of biomedical signals for external or internal reasons of the human body. Therefore, to obtain a more accurate signal, it is needed to select filtering techniques that minimize the noise. In this study, the techniques used are empirical mode decomposition and its variants. Among the new versions of variants is the improved complete ensemble empirical mode decomposition with adaptive noise. These methods are applied to electroencephalogram and electromyogram signals corrupted by natural noise and white Gaussian noise. The obtained results through the use of the improved complete ensemble empirical mode decomposition with adaptive noises how the high performance that includes minimizing the noise and the effectiveness of the components of the signals used in the present research. This method has low values of the mean square error and high values of signal-to-noise ratio compared to other methods used in this study.

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“…One of the major biomedical signal issues such as an electromyogram (EMG) and an electrocardiogram (ECG) is denoising issues. Especially for ECG, the issue is the distinction between the desired signal and the noise or interference caused by the 49 to 51 Hz powerline interference, muscle artifacts, wandering, or electrode artifacts [1], [2]. Besides that, for an ECG signal of 50 Hz, frequencies from 47 Hz to 53 Hz are more important.…”

Section: Introductionmentioning

confidence: 99%

“…If the stopband is very narrow, it is called a notch filter. The transfer function of the notch is shown in (1).…”

Section: Introductionmentioning

confidence: 99%

An approach of adaptive notch filtering design for electrocardiogram noise cancellation

Hidayat

Lestari

Herawati

et al. 2021

IJEECS

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An electrocardiogram (ECG) is a means of measuring and monitoring important signals from heart activity. One of the major biomedical signal issues such as ECG is the issue of separating the desired signal from noise or interference. Different kinds of digital filters are used to distinguish the signal components from the unwanted frequency range to the ECG signal. To address the question of noise to the ECG signal, in this paper the digital notch filter IIR 47 Hz is designed and simulated to demonstrate the elimination of 47 Hz noise to obtain an accurate ECG signal. The full architecture of the structure and coefficient of the IIR notch filter was carried out using the FDA Tool. Then the model is finished with the help of Simulink and the MATLAB script was to filter out the 47 Hz noise from the signal of ECG. For this purpose, the normalized least mean square (NLMS) algorithm was used. The results indicate that before being filtered and after being filtered it clearly shows the elimination of 47 Hz noise in the signal of the ECG. These results also show the accuracy of the design technique and provide an easy model to filter out noise in the ECG signal.

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Filtering and analyzing normal and abnormal electromyogram signals

Cited by 9 publications

References 17 publications

Analysis Electroencephalogram Signals Using Denoising and Time-Frequency Techniques

Analysis Electroencephalogram Signals Using Denoising and Time-Frequency Techniques

Denoising electromyogram and electroencephalogram signals using improved complete ensemble empirical mode decomposition with adaptive noise

An approach of adaptive notch filtering design for electrocardiogram noise cancellation

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