Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis

Mijović, Bogdan; Vos, Maarten De; Gligorijević, Ivan; Taelman, Joachim; Huffel, Sabine Van

doi:10.1109/tbme.2010.2051440

Cited by 328 publications

(223 citation statements)

References 18 publications

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“…Classification may be desired, for example, to indentify pathological beats and subsequently determine the pathology. Whereas ICA is usually applied to a set of a few concurrently measured ECG signals, such as 12-lead ECG, ICA based methods for single-channel ECG signals have also been proposed, e.g., by de Chazal et al (2003) and Mijović (2010). The other extreme is represented by ICA of high-density ECG measurements with tens (Zhu et al, 2008) or even hundreds of ECG lead signals used as ICA input to achieve enhanced level of source separation.…”

Section: Ica In Ecg Signal Processingmentioning

confidence: 99%

Independent Component Analysis in ECG Signal Processing

Tanskanen¹,

Viik²

2012

Advances in Electrocardiograms - Methods and Analysis

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Section: Ica In Ecg Signal Processingmentioning

confidence: 99%

Independent Component Analysis in ECG Signal Processing

Tanskanen¹,

Viik²

2012

Advances in Electrocardiograms - Methods and Analysis

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“…EMD has been successfully applied to solve numerous practical problems in various applications [2][3][4][5][6][7][8][9]. This technique decomposes a time series into a set of zero-mean underlying components called intrinsic mode functions (IMF).…”

Section: Introductionmentioning

confidence: 99%

Noise Cancellation from Vibrocardiographic Signals Based on the Ensemble Empirical Mode Decomposition

TaebiA¹,

Ha²

2017

JABB

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Vibrocardiographic (VCG) signals are the cardiac vibration measured at the chest surface. These signals can contain useful information for diagnosing cardiac conditions but are often contaminated by noise. Although band-pass and adaptive filters were used for noise removal from similar signals, the utility of ensemble empirical mode decomposition (EEMD) for filtering VCG was not previously investigated. In this study, an EEMD-based filter was proposed and tested. The filtering scheme first decomposed the VCG waveform into a set of intrinsic mode functions (IMF) then utilized the partial sum of IMFs to remove white noise that was added to simulated VCG signals. To measure the filter effectiveness, the normalized root-mean-square error (NRMSE) between the clean (i.e., before adding noise) and filtered signals was calculated for signal-to-noise ratios ranging from 1 to 20 dB. The EEMD-based filter performance was also compared with traditional methods such as Wiener filter. This comparison suggested that EEMDbased filter outperformed the Wiener filter in noise removal from simulated VCG. These results also suggested that EEMD may be utilized for white noise removal from actual VCG signals. Further investigations are warranted to study the relation between IMFs and different types of noise, which can enhance the effectiveness of EEMD-based filters in removing these noise types from actual VCG signals.

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“…The process cannot be performed automatically and quantitatively, because there are no appropriate parameters that can distinguish the independent components between MCG signals and noise using measurement data. In addition, the component selection process has not been discussed in detail up until now 1)- 6) .…”

Section: Introductionmentioning

confidence: 99%

Automatic component selection for noise reduction in magnetocardiograph based on independent component analysis

et al. 2017

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Magnetocardiogram (MCG) measurement systems require noise reduction, because MCG signals are extremely small compared to environmental magnetic noise. We investigate the efficacy of a novel noise-reduction method, based on an independent component analysis (ICA). The proposed noise reduction method requires a component selection process to distinguish signal from noise. A major challenge in applying ICA-based noise reduction method is the selection of suitable parameters, which in practice is often performed manually with rather subjective parameter choices. To address this issue, we proposed a component selection method that can be performed quantitatively and automatically. The proposed method is based on the peak values of the autocorrelation function and helps distinguish the independent components of the MCG signals from the noise using an appropriate threshold. By using the proposed method, we obtain output signal-to-noise ratios (SNRs) of 33.98 dB, 19.17 dB, and 13.56 dB, corresponding to input SNRs for the simulated data at respectively 0 dB, -10 dB, and -20 dB, after noise reduction. The results show that the proposed method exhibits remarkable promise in extracting a noise-mitigated MCG signal for a wide range of SNRs.

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Source Separation From Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis

Cited by 328 publications

References 18 publications

Independent Component Analysis in ECG Signal Processing

Independent Component Analysis in ECG Signal Processing

Noise Cancellation from Vibrocardiographic Signals Based on the Ensemble Empirical Mode Decomposition

Automatic component selection for noise reduction in magnetocardiograph based on independent component analysis

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