Combination of the CEEM Decomposition with Adaptive Noise and Periodogram Technique for ECG Signals Analysis

Dliou, Azzedine; Elouaham, Samir; Latif, Rachid; Laaboubi, Mostafa

doi:10.5772/intechopen.86007

Cited by 4 publications

(6 citation statements)

References 18 publications

(52 reference statements)

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“…The CEEMDAN technique is based on EMD. The set of rules and basics ofthe EMD theory and its variant techniques are given in [17]- [19]. The CEEMDAN method is presented as follows:…”

Section: ( ) ( ) ( ) R T Z T Imf T = −mentioning

confidence: 99%

“…The pitfalls that appeared in EMD and EEMD were overcome by applying complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) that uses the noise auxiliary. This method gives a correct restoration of biomedical signals and perfect spectra of the decomposed mode [17]- [19]. The iterations presented in EEMD are more than iterations of the CEEMDAN method.…”

Section: Introductionmentioning

confidence: 99%

“…The CN noise interfered with the real and clean EEG and EMG signals, and the white noise is added with different values of the signal-to-noise ratio (SNR). CN composed noise is given by following equation [17]:…”

mentioning

confidence: 99%

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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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“…The CEEMDAN technique is based on EMD. The set of rules and basics ofthe EMD theory and its variant techniques are given in [17]- [19]. The CEEMDAN method is presented as follows:…”

Section: ( ) ( ) ( ) R T Z T Imf T = −mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…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%

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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Empirical Wavelet Transform Based ECG Signal Filtering Method

Elouaham,

Dliou,

Jenkal

et al. 2024

Journal of Electrical and Computer Engineering

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The electrocardiogram (ECG) is a diagnostic tool that provides insights into the heart’s electrical activity and overall health. However, internal and external noises complicate accurate heart issue diagnosis. Noise in the ECG signal distorts and introduces artifacts, making it difficult to detect subtle abnormalities. To ensure an accurate evaluation, noise-free ECG signals are crucial. This study introduces the empirical wavelet transform (EWT), a contemporary denoising method. EWT decomposes the signal into frequency components, allowing detailed analysis by constructing a customized wavelet basis. Researchers and practitioners can enhance signal analysis by separating the desired components from unwanted noise. The EWT approach effectively eliminates noise while maintaining signal information. The study applies DWT-ADTF, FST, Kalman, Liouville–Weyl fractional compound integral filter LW, Weiner, and EWT denoising methods to two ECG databases from MIT-BIH, which encompass a wide range of cardiac signals and noise levels. The comparative analysis highlights EWT’s strengths through improved signal quality and objective performance metrics. This adaptive transform proves promising for denoising ECG signals and facilitating accurate analysis in clinical and research settings.

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Combination of the CEEM Decomposition with Adaptive Noise and Periodogram Technique for ECG Signals Analysis

Cited by 4 publications

References 18 publications

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

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

Analysis Electroencephalogram Signals Using Denoising and Time-Frequency Techniques

Empirical Wavelet Transform Based ECG Signal Filtering Method

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