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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

Scatterplot matrix of results obtained with neural signals.The off-diagonal elements are scatterplots of the signal amplitudes and the diagonal elements are Fourier transformations of the signals. For each of the off-diagonal elements, the abscissa is the label of nearest diagonal element above or below and the ordinate is the label of the nearest diagonal element on its side. For example, on the first row and second column the amplitudes of neural signals without noise is compared with the same version adulterated with powerline noise using a scatterplot. On the first row and third column, neural signals without noise is compared with a reconstructed version using a 4th order Butterworth filter. On the first row and fourth column, the results obtained via the proposed approach is compared with the original neural signals. This convention is used throughout the figure.
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fig-4: Scatterplot matrix of results obtained with neural signals.The off-diagonal elements are scatterplots of the signal amplitudes and the diagonal elements are Fourier transformations of the signals. For each of the off-diagonal elements, the abscissa is the label of nearest diagonal element above or below and the ordinate is the label of the nearest diagonal element on its side. For example, on the first row and second column the amplitudes of neural signals without noise is compared with the same version adulterated with powerline noise using a scatterplot. On the first row and third column, neural signals without noise is compared with a reconstructed version using a 4th order Butterworth filter. On the first row and fourth column, the results obtained via the proposed approach is compared with the original neural signals. This convention is used throughout the figure.

Mentions: Sub-cortical local field potentials and extra-cranial EEGs were resampled to have equal sampling rate, concatenated and adulterated with artificial powerline noise. A band-stop 4th order Butterworth filter was used to filter out signals between 59.5 Hz and 60.5 Hz, and the proposed framework was also applied to the adulterated signal. The introduction of powerline noise introduced a slight amplitude decorrelation between the original signal and the adulterated signal. The aim of Fig. 4 was to characterize the level of re-correlation after removing the powerline noise using the proposed approach and the mentioned infinite impulse response filter (IIR filter). It was noted that although the frequency spectra indicated that both methods expelled powerline noise, their correlations differed minutely. The proposed approach recovered the signals with a slightly higher correlation than the named IIR filter (proposed = 1.0000, Butterworth filter = 0.9999).


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

Akwei-Sekyere S - PeerJ (2015)

Scatterplot matrix of results obtained with neural signals.The off-diagonal elements are scatterplots of the signal amplitudes and the diagonal elements are Fourier transformations of the signals. For each of the off-diagonal elements, the abscissa is the label of nearest diagonal element above or below and the ordinate is the label of the nearest diagonal element on its side. For example, on the first row and second column the amplitudes of neural signals without noise is compared with the same version adulterated with powerline noise using a scatterplot. On the first row and third column, neural signals without noise is compared with a reconstructed version using a 4th order Butterworth filter. On the first row and fourth column, the results obtained via the proposed approach is compared with the original neural signals. This convention is used throughout the figure.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig-4: Scatterplot matrix of results obtained with neural signals.The off-diagonal elements are scatterplots of the signal amplitudes and the diagonal elements are Fourier transformations of the signals. For each of the off-diagonal elements, the abscissa is the label of nearest diagonal element above or below and the ordinate is the label of the nearest diagonal element on its side. For example, on the first row and second column the amplitudes of neural signals without noise is compared with the same version adulterated with powerline noise using a scatterplot. On the first row and third column, neural signals without noise is compared with a reconstructed version using a 4th order Butterworth filter. On the first row and fourth column, the results obtained via the proposed approach is compared with the original neural signals. This convention is used throughout the figure.
Mentions: Sub-cortical local field potentials and extra-cranial EEGs were resampled to have equal sampling rate, concatenated and adulterated with artificial powerline noise. A band-stop 4th order Butterworth filter was used to filter out signals between 59.5 Hz and 60.5 Hz, and the proposed framework was also applied to the adulterated signal. The introduction of powerline noise introduced a slight amplitude decorrelation between the original signal and the adulterated signal. The aim of Fig. 4 was to characterize the level of re-correlation after removing the powerline noise using the proposed approach and the mentioned infinite impulse response filter (IIR filter). It was noted that although the frequency spectra indicated that both methods expelled powerline noise, their correlations differed minutely. The proposed approach recovered the signals with a slightly higher correlation than the named IIR filter (proposed = 1.0000, Butterworth filter = 0.9999).

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