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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.


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Comparison of the proposed method with other approaches.The proposed approach performed better than others at all SNRs considered.
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fig-8: Comparison of the proposed method with other approaches.The proposed approach performed better than others at all SNRs considered.

Mentions: The performance of the proposed approach was compared with that of a 4th order band-stop Butterworth filter, EEMD, ICA and a combination of EEMD and ICA (EEMD-ICA). The log-mean squared error between the original signal and the signal recovered after eliminating powerline noise was used as a parameter to compare the performance of the approaches. With Fig. 8, it is shown that the proposed approach performed better than the approaches mentioned previously. It can be noted that EEMD, ICA and EEMD-ICA had comparable results from a broad perspective; however ICA performed slightly better under high SNRs. From this, it is plausible that the reason why EEMD-ICA had lower log-mean squared errors at higher SNRs than EEMD alone was due to the effect of ICA. The mentioned infinite impulse response filter—which is a state-of-the-art technique—proved to be better at eliminating powerline noise than all the mentioned approaches except the proposed framework. In contrast with the other techniques, the change in performance (from a log-mean squared error point of view) plateaued rather quickly at SNRs greater than 0dB; however, it still maintained the highest performance across all SNRs evaluated. This is a strong indication that the proposed approach works relatively better at low SNRs.


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

Akwei-Sekyere S - PeerJ (2015)

Comparison of the proposed method with other approaches.The proposed approach performed better than others at all SNRs considered.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig-8: Comparison of the proposed method with other approaches.The proposed approach performed better than others at all SNRs considered.
Mentions: The performance of the proposed approach was compared with that of a 4th order band-stop Butterworth filter, EEMD, ICA and a combination of EEMD and ICA (EEMD-ICA). The log-mean squared error between the original signal and the signal recovered after eliminating powerline noise was used as a parameter to compare the performance of the approaches. With Fig. 8, it is shown that the proposed approach performed better than the approaches mentioned previously. It can be noted that EEMD, ICA and EEMD-ICA had comparable results from a broad perspective; however ICA performed slightly better under high SNRs. From this, it is plausible that the reason why EEMD-ICA had lower log-mean squared errors at higher SNRs than EEMD alone was due to the effect of ICA. The mentioned infinite impulse response filter—which is a state-of-the-art technique—proved to be better at eliminating powerline noise than all the mentioned approaches except the proposed framework. In contrast with the other techniques, the change in performance (from a log-mean squared error point of view) plateaued rather quickly at SNRs greater than 0dB; however, it still maintained the highest performance across all SNRs evaluated. This is a strong indication that the proposed approach works relatively better at low SNRs.

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