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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

Comparison of power spectra.(A) The red trace represents the Manhattan distance between the Fourier transformation of the proposed reconstruction procedure and the original signal without artificial noise. Similarly, the blue trace is the Manhattan distance between the Fourier transformation of the signal obtained via band-stop Butterworth filtering and the original signal without artificial noise. (B) The red trace is a scatterplot of the Fourier transform of the original signal and the reconstructed signal using the proposed approach. Likewise, the blue trace represents a scatterplot of the Fourier transform of the original signal and the reconstructed signal using a band-stop Butterworth filter. Pearson’s correlation coefficient for both were 1.0000.
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fig-5: Comparison of power spectra.(A) The red trace represents the Manhattan distance between the Fourier transformation of the proposed reconstruction procedure and the original signal without artificial noise. Similarly, the blue trace is the Manhattan distance between the Fourier transformation of the signal obtained via band-stop Butterworth filtering and the original signal without artificial noise. (B) The red trace is a scatterplot of the Fourier transform of the original signal and the reconstructed signal using the proposed approach. Likewise, the blue trace represents a scatterplot of the Fourier transform of the original signal and the reconstructed signal using a band-stop Butterworth filter. Pearson’s correlation coefficient for both were 1.0000.

Mentions: In order to understand the subtle differences between the proposed method and the mentioned IIR filter, the Manhattan distance between the Fourier transformation of the original signal which had no powerline noise and the denoised signal using both approaches were evaluated (Fig. 5). It is shown that the difference between the power spectrum of the original signal and the one reconstructed using the proposed approach was smaller than that of the band-stop Butterworth filter. This was further validated via a Wilcoxon rank-sum test; the p-value obtained by comparing the original power spectrum with that of the one reconstructed using the proposed approach was 0.9903, while that of the band-stop Butterworth filter was 0.7627. In spite of the fact that there was no statistically significant difference between the reconstructed power spectra and that of the original, there was a statistically significant difference between the power spectrum of the signals reconstructed using the suggested algorithm and that of the band-stop IIR filter (p-value < 0.0001). On a grand scale, the power spectra of the denoised signals were highly correlated. This is an indication that there was a relatively similar response at the powerline noise frequency for neural signals.


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

Akwei-Sekyere S - PeerJ (2015)

Comparison of power spectra.(A) The red trace represents the Manhattan distance between the Fourier transformation of the proposed reconstruction procedure and the original signal without artificial noise. Similarly, the blue trace is the Manhattan distance between the Fourier transformation of the signal obtained via band-stop Butterworth filtering and the original signal without artificial noise. (B) The red trace is a scatterplot of the Fourier transform of the original signal and the reconstructed signal using the proposed approach. Likewise, the blue trace represents a scatterplot of the Fourier transform of the original signal and the reconstructed signal using a band-stop Butterworth filter. Pearson’s correlation coefficient for both were 1.0000.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig-5: Comparison of power spectra.(A) The red trace represents the Manhattan distance between the Fourier transformation of the proposed reconstruction procedure and the original signal without artificial noise. Similarly, the blue trace is the Manhattan distance between the Fourier transformation of the signal obtained via band-stop Butterworth filtering and the original signal without artificial noise. (B) The red trace is a scatterplot of the Fourier transform of the original signal and the reconstructed signal using the proposed approach. Likewise, the blue trace represents a scatterplot of the Fourier transform of the original signal and the reconstructed signal using a band-stop Butterworth filter. Pearson’s correlation coefficient for both were 1.0000.
Mentions: In order to understand the subtle differences between the proposed method and the mentioned IIR filter, the Manhattan distance between the Fourier transformation of the original signal which had no powerline noise and the denoised signal using both approaches were evaluated (Fig. 5). It is shown that the difference between the power spectrum of the original signal and the one reconstructed using the proposed approach was smaller than that of the band-stop Butterworth filter. This was further validated via a Wilcoxon rank-sum test; the p-value obtained by comparing the original power spectrum with that of the one reconstructed using the proposed approach was 0.9903, while that of the band-stop Butterworth filter was 0.7627. In spite of the fact that there was no statistically significant difference between the reconstructed power spectra and that of the original, there was a statistically significant difference between the power spectrum of the signals reconstructed using the suggested algorithm and that of the band-stop IIR filter (p-value < 0.0001). On a grand scale, the power spectra of the denoised signals were highly correlated. This is an indication that there was a relatively similar response at the powerline noise frequency for neural signals.

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