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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

Learning the amplitude of powerline noise in a natural context.(A) The green trace is the original signal and the blue trace is the reconstructed signal after removing natural 50 Hz AC noise. (B) The green trace represents the power spectrum for the original signal and the blue one represents that of the reconstructed signal. (C) This shows the process by which the noise amplitude is learned by the algorithm.
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fig-3: Learning the amplitude of powerline noise in a natural context.(A) The green trace is the original signal and the blue trace is the reconstructed signal after removing natural 50 Hz AC noise. (B) The green trace represents the power spectrum for the original signal and the blue one represents that of the reconstructed signal. (C) This shows the process by which the noise amplitude is learned by the algorithm.

Mentions: In accordance with the fact that the problem of deconvolution is ill-posed, the solutions are not unique; this issue is tackled by learning the appropriate amplitude for the noise model. To exhibit the amplitude learning procedure, an EEG with natural 50 Hz noise was denoised using the proposed framework. As presented in Fig. 3C, the learning procedure aimed at minimizing the cost function has a unique solution. This indicates that there is some level of consistency in the process. In view of the fact that there is virtually no ground truth with natural AC noise, the power spectra of the original signal and the denoised signal were compared. The traces in Fig. 3A show that there is little difference between the original signal and the recovered signal. However, Fig. 3B reveals that this manifestation is primarily due to the low amplitude of the 50 Hz AC noise. Although the amplitude is very small, the algorithm was able to extinguish an appreciable amount of powerline noise. The same figure indicates that if the amplitude of the AC noise is small enough, it might go unnoticed after Fourier transformation. Further, in order to obtain an accurate view of how a biomedical signal behaves under various frequency specifications, it is better to employ the wavelet transform (which is a convolution between a wavelet and a signal) rather than the Fourier transform. This is due to the fact that the Fourier transform assumes the signal being transformed is of a sinusoidal origin (Bloomfield, 2004), which is not true in most cases. Although the short-term Fourier transform is used to avoid this pitfall, the wavelet transform (which handles frequencies in a logarithmic fashion) adapts better to highly variable signals (Daubechies, 1990). The suggested algorithm employed a transformation analytically similar to the wavelet transform. By the same token, the frequency properties revealed by a summation of the pseudo-convolution between the real component of the Morlet wavelet and the signals aided in reliably in extracting powerline noise; this, in turn, provided a solid model foundation for learning the most appropriate amplitude.


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

Akwei-Sekyere S - PeerJ (2015)

Learning the amplitude of powerline noise in a natural context.(A) The green trace is the original signal and the blue trace is the reconstructed signal after removing natural 50 Hz AC noise. (B) The green trace represents the power spectrum for the original signal and the blue one represents that of the reconstructed signal. (C) This shows the process by which the noise amplitude is learned by the algorithm.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig-3: Learning the amplitude of powerline noise in a natural context.(A) The green trace is the original signal and the blue trace is the reconstructed signal after removing natural 50 Hz AC noise. (B) The green trace represents the power spectrum for the original signal and the blue one represents that of the reconstructed signal. (C) This shows the process by which the noise amplitude is learned by the algorithm.
Mentions: In accordance with the fact that the problem of deconvolution is ill-posed, the solutions are not unique; this issue is tackled by learning the appropriate amplitude for the noise model. To exhibit the amplitude learning procedure, an EEG with natural 50 Hz noise was denoised using the proposed framework. As presented in Fig. 3C, the learning procedure aimed at minimizing the cost function has a unique solution. This indicates that there is some level of consistency in the process. In view of the fact that there is virtually no ground truth with natural AC noise, the power spectra of the original signal and the denoised signal were compared. The traces in Fig. 3A show that there is little difference between the original signal and the recovered signal. However, Fig. 3B reveals that this manifestation is primarily due to the low amplitude of the 50 Hz AC noise. Although the amplitude is very small, the algorithm was able to extinguish an appreciable amount of powerline noise. The same figure indicates that if the amplitude of the AC noise is small enough, it might go unnoticed after Fourier transformation. Further, in order to obtain an accurate view of how a biomedical signal behaves under various frequency specifications, it is better to employ the wavelet transform (which is a convolution between a wavelet and a signal) rather than the Fourier transform. This is due to the fact that the Fourier transform assumes the signal being transformed is of a sinusoidal origin (Bloomfield, 2004), which is not true in most cases. Although the short-term Fourier transform is used to avoid this pitfall, the wavelet transform (which handles frequencies in a logarithmic fashion) adapts better to highly variable signals (Daubechies, 1990). The suggested algorithm employed a transformation analytically similar to the wavelet transform. By the same token, the frequency properties revealed by a summation of the pseudo-convolution between the real component of the Morlet wavelet and the signals aided in reliably in extracting powerline noise; this, in turn, provided a solid model foundation for learning the most appropriate amplitude.

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