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Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis.

Akwei-Sekyere S - PeerJ (2015)

Bottom Line: However, due to the instability of biomedical signals, the distribution of signals filtered out may not be centered at 50/60 Hz.Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw recording and subtract it from the raw data in order to obtain a relatively clean signal.The performance of this algorithm was compared with that of a 4th order band-stop Butterworth filter, empirical mode decomposition, independent component analysis and, a combination of empirical mode decomposition with independent component analysis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Neuroscience Program, Michigan State University , East Lansing, MI , USA.

ABSTRACT
The distortion of biomedical signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of the data. Usually, powerline noise in biomedical recordings are extinguished via band-stop filters. However, due to the instability of biomedical signals, the distribution of signals filtered out may not be centered at 50/60 Hz. As a result, self-correction methods are needed to optimize the performance of these filters. Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw recording and subtract it from the raw data in order to obtain a relatively clean signal. This paper proposes a method that utilizes this approach by decomposing the recorded signal and extracting powerline noise via blind source separation and wavelet analysis. The performance of this algorithm was compared with that of a 4th order band-stop Butterworth filter, empirical mode decomposition, independent component analysis and, a combination of empirical mode decomposition with independent component analysis. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned techniques, especially at low signal-to-noise ratio.

No MeSH data available.


Related in: MedlinePlus

Ensemble empirical mode decomposition on an ECG signal with 60 Hz powerline noise added to it and the frequency spectra of the resulting intrinsic mode functions.(A) On the left is the original snippet of an ECG signal corrupted with powerline noise (green). Seven IMFs obtained via EEMD have been shown on the right. A constriction of the windows of IMF 4 and IMF 5 reveals that a chronic sinusoidal modulation exists within them. (B) The frequency spectra of the IMFs shown in A have been plotted. From this, it is plausible that the modulations seen in IMFs 4 and 5 are powerline noise at 60 Hz.
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fig-1: Ensemble empirical mode decomposition on an ECG signal with 60 Hz powerline noise added to it and the frequency spectra of the resulting intrinsic mode functions.(A) On the left is the original snippet of an ECG signal corrupted with powerline noise (green). Seven IMFs obtained via EEMD have been shown on the right. A constriction of the windows of IMF 4 and IMF 5 reveals that a chronic sinusoidal modulation exists within them. (B) The frequency spectra of the IMFs shown in A have been plotted. From this, it is plausible that the modulations seen in IMFs 4 and 5 are powerline noise at 60 Hz.

Mentions: Due to the fact that IMFs obtained via EEMD have the attribute of being amplitude–frequency modulations (Fig. 1A), it is essential to un-mix the cocktail into their statistically independent sources. In principle, independent constituents of y(t) with specific frequencies—such as a chronic sinusoidal xHz noise—can be extracted with the aid of ci(t) and r(t). However, due to the high level of variability in the frequency spectrum for each IMF and the occasional overlap in their frequency spectra (Fig. 1B), selecting which IMFs will undergo independent component analysis will help reduce the probability of having noisy estimates of source components. Each IMF selected should be a putative powerline noise in accordance with the properties of its frequency.


Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis.

Akwei-Sekyere S - PeerJ (2015)

Ensemble empirical mode decomposition on an ECG signal with 60 Hz powerline noise added to it and the frequency spectra of the resulting intrinsic mode functions.(A) On the left is the original snippet of an ECG signal corrupted with powerline noise (green). Seven IMFs obtained via EEMD have been shown on the right. A constriction of the windows of IMF 4 and IMF 5 reveals that a chronic sinusoidal modulation exists within them. (B) The frequency spectra of the IMFs shown in A have been plotted. From this, it is plausible that the modulations seen in IMFs 4 and 5 are powerline noise at 60 Hz.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493666&req=5

fig-1: Ensemble empirical mode decomposition on an ECG signal with 60 Hz powerline noise added to it and the frequency spectra of the resulting intrinsic mode functions.(A) On the left is the original snippet of an ECG signal corrupted with powerline noise (green). Seven IMFs obtained via EEMD have been shown on the right. A constriction of the windows of IMF 4 and IMF 5 reveals that a chronic sinusoidal modulation exists within them. (B) The frequency spectra of the IMFs shown in A have been plotted. From this, it is plausible that the modulations seen in IMFs 4 and 5 are powerline noise at 60 Hz.
Mentions: Due to the fact that IMFs obtained via EEMD have the attribute of being amplitude–frequency modulations (Fig. 1A), it is essential to un-mix the cocktail into their statistically independent sources. In principle, independent constituents of y(t) with specific frequencies—such as a chronic sinusoidal xHz noise—can be extracted with the aid of ci(t) and r(t). However, due to the high level of variability in the frequency spectrum for each IMF and the occasional overlap in their frequency spectra (Fig. 1B), selecting which IMFs will undergo independent component analysis will help reduce the probability of having noisy estimates of source components. Each IMF selected should be a putative powerline noise in accordance with the properties of its frequency.

Bottom Line: However, due to the instability of biomedical signals, the distribution of signals filtered out may not be centered at 50/60 Hz.Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw recording and subtract it from the raw data in order to obtain a relatively clean signal.The performance of this algorithm was compared with that of a 4th order band-stop Butterworth filter, empirical mode decomposition, independent component analysis and, a combination of empirical mode decomposition with independent component analysis.

View Article: PubMed Central - HTML - PubMed

Affiliation: Neuroscience Program, Michigan State University , East Lansing, MI , USA.

ABSTRACT
The distortion of biomedical signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of the data. Usually, powerline noise in biomedical recordings are extinguished via band-stop filters. However, due to the instability of biomedical signals, the distribution of signals filtered out may not be centered at 50/60 Hz. As a result, self-correction methods are needed to optimize the performance of these filters. Since powerline noise is additive in nature, it is intuitive to model powerline noise in a raw recording and subtract it from the raw data in order to obtain a relatively clean signal. This paper proposes a method that utilizes this approach by decomposing the recorded signal and extracting powerline noise via blind source separation and wavelet analysis. The performance of this algorithm was compared with that of a 4th order band-stop Butterworth filter, empirical mode decomposition, independent component analysis and, a combination of empirical mode decomposition with independent component analysis. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned techniques, especially at low signal-to-noise ratio.

No MeSH data available.


Related in: MedlinePlus