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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 and waveforms.(A) The red, green, black and blue traces are the adulterated signal, original signal, signal obtained after Butterworth filter and signal obtained using the proposed denoising framework. (B) The waveforms detected via k-means without dimensionality reduction. (C) The blue signal is the Manhattan distance between the Fourier transform of the results from the proposed approach and the original signal. Similarly, the black trace is the Manhattan distance between the Fourier transform of the signal obtained via Butterworth filtering and the original signal.
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fig-6: Comparison of power spectra and waveforms.(A) The red, green, black and blue traces are the adulterated signal, original signal, signal obtained after Butterworth filter and signal obtained using the proposed denoising framework. (B) The waveforms detected via k-means without dimensionality reduction. (C) The blue signal is the Manhattan distance between the Fourier transform of the results from the proposed approach and the original signal. Similarly, the black trace is the Manhattan distance between the Fourier transform of the signal obtained via Butterworth filtering and the original signal.

Mentions: Electrocardiogram signals without powerline noise were corrupted with artificial 60 Hz powerline noise. Thereafter, they were reconstructed via band-stop Butterworth filtering and the proposed method. The results obtained were almost identical in the time series (Fig. 6A), however there was a substantial difference circa 60 Hz. Similar to the results obtained with neural signals, the power of the extract using the proposed method at 60 Hz was lower than that of the Butterworth filter. One of the many properties of ECG signals sought after is the waveform. In accordance with the knowledge that the ECG signal used was a concatenation of three data sets with two electrode recordings each, it is expected that at least six different clusters ECG waveforms can be detected. The waveforms were detected via simple thresholding and were partitioned via the k-means algorithm. Naturally, clustering such high dimensional data requires dimensionality reduction via principal component analysis or laplacian eigenmaps. This clustering process was done without dimensionality reduction in order to accurately compare the effect of the proposed approach and that of the Butterworth filter on the reconstructed signal. As shown in Fig. 6B, although the labels of the classes were different, their elements were not. This further proves that the relative temporal morphology of each reconstructed signal is preserved.


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

Akwei-Sekyere S - PeerJ (2015)

Comparison of power spectra and waveforms.(A) The red, green, black and blue traces are the adulterated signal, original signal, signal obtained after Butterworth filter and signal obtained using the proposed denoising framework. (B) The waveforms detected via k-means without dimensionality reduction. (C) The blue signal is the Manhattan distance between the Fourier transform of the results from the proposed approach and the original signal. Similarly, the black trace is the Manhattan distance between the Fourier transform of the signal obtained via Butterworth filtering and the original signal.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig-6: Comparison of power spectra and waveforms.(A) The red, green, black and blue traces are the adulterated signal, original signal, signal obtained after Butterworth filter and signal obtained using the proposed denoising framework. (B) The waveforms detected via k-means without dimensionality reduction. (C) The blue signal is the Manhattan distance between the Fourier transform of the results from the proposed approach and the original signal. Similarly, the black trace is the Manhattan distance between the Fourier transform of the signal obtained via Butterworth filtering and the original signal.
Mentions: Electrocardiogram signals without powerline noise were corrupted with artificial 60 Hz powerline noise. Thereafter, they were reconstructed via band-stop Butterworth filtering and the proposed method. The results obtained were almost identical in the time series (Fig. 6A), however there was a substantial difference circa 60 Hz. Similar to the results obtained with neural signals, the power of the extract using the proposed method at 60 Hz was lower than that of the Butterworth filter. One of the many properties of ECG signals sought after is the waveform. In accordance with the knowledge that the ECG signal used was a concatenation of three data sets with two electrode recordings each, it is expected that at least six different clusters ECG waveforms can be detected. The waveforms were detected via simple thresholding and were partitioned via the k-means algorithm. Naturally, clustering such high dimensional data requires dimensionality reduction via principal component analysis or laplacian eigenmaps. This clustering process was done without dimensionality reduction in order to accurately compare the effect of the proposed approach and that of the Butterworth filter on the reconstructed signal. As shown in Fig. 6B, although the labels of the classes were different, their elements were not. This further proves that the relative temporal morphology of each reconstructed signal is preserved.

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