title: Powerline noise elimination in biomedical signals via blind source separation and wavelet analysis.
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.
authors.affiliation: Neuroscience Program, Michigan State University , East Lansing, MI , USA.
authors.forename: Samuel
authors.initials: S
authors.lastname: Akwei-Sekyere
keywords: Electrophysiology; Ensemble empirical mode decomposition; Independent component analysis; Machine learning; Neurotechnology; Noise-assisted noise reduction; Wavelet