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

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1086 ◽  
Author(s):  
Samuel Akwei-Sekyere
Keyword(s):  
Wavelet Analysis ◽  
Blind Source Separation ◽  
Source Separation ◽  
Noise Elimination ◽  
Biomedical Signals

Blind source separation problem algorithms for audio and biomedical signals

2018 ◽  
Author(s):  
Cesar Javier Ortiz-Echeverri ◽  
Perez Karla Daniela ◽  
B. Giovanni Galindo-Burgos ◽  
Juvenal Rodriguez-Resendiz
Keyword(s):  
Blind Source Separation ◽  
Source Separation ◽  
Separation Problem ◽  
Biomedical Signals

scholarly journals Powerline noise elimination in neural signals via blind source separation and wavelet analysis

2014 ◽  
Author(s):  
Samuel Akwei-Sekyere
Keyword(s):  
Wavelet Analysis ◽  
Blind Source Separation ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Source Separation ◽  
Finite Impulse Response Filter ◽  
Electrophysiological Recording ◽  
Neural Signals ◽  
Neural Data ◽  
Impulse Response Filter

The distortion of neural signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of neural data. State of the art electrophysiology employs band-stop filters with which powerline noise are attenuated to low-amplitudes. Due to the instability of neural signals, the distribution of signals filtered out may not be centered at \(50/60Hz\). 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 electrophysiological recording and subtract it from the raw data in order to obtain neural data. 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 band-stop finite impulse response filter. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned band-stop finite impulse response filter, especially at low signal-to-noise ratio.

scholarly journals Powerline noise elimination in neural signals via blind source separation and wavelet analysis

2014 ◽  
Author(s):  
Samuel Akwei-Sekyere
Keyword(s):  
Wavelet Analysis ◽  
Blind Source Separation ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Source Separation ◽  
Finite Impulse Response Filter ◽  
Electrophysiological Recording ◽  
Neural Signals ◽  
Neural Data ◽  
Impulse Response Filter

The distortion of neural signals by powerline noise from recording biomedical devices has the potential to reduce the quality and convolute the interpretations of neural data. State of the art electrophysiology employs band-stop filters with which powerline noise are attenuated to low-amplitudes. Due to the instability of neural signals, the distribution of signals filtered out may not be centered at \(50/60Hz\). 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 electrophysiological recording and subtract it from the raw data in order to obtain neural data. 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 band-stop finite impulse response filter. The proposed method was able to expel sinusoidal signals within powerline noise frequency range with higher fidelity in comparison with the mentioned band-stop finite impulse response filter, especially at low signal-to-noise ratio.

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