TY - JOUR
T1 - An adaptive filter for the removal of drifting sinusoidal noise without a reference
AU - Kelly, John W.
AU - Siewiorek, Daniel P.
AU - Smailagic, Asim
AU - Wang, Wei
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2016/1
Y1 - 2016/1
N2 - This paper presents a method for filtering sinusoidal noise with a variable bandwidth filter that is capable of tracking a sinusoids drifting frequency. The method, which is based on the adaptive noise canceling (ANC) technique, will be referred to here as the adaptive sinusoid canceler (ASC). The ASC eliminates sinusoidal contamination by tracking its frequency and achieving a narrower bandwidth than typical notch filters. The detected frequency is used to digitally generate an internal reference instead of relying on an external one as ANC filters typically do. The filters bandwidth adjusts to achieve faster and more accurate convergence. In this paper, the focus of the discussion and the data is physiological signals, specifically electrocorticographic (ECoG) neural data contaminated with power line noise, but the presented technique could be applicable to other recordings as well. On simulated data, the ASC was able to reliably track the noises frequency, properly adjust its bandwidth, and outperform comparativemethods including standard notch filters and an adaptive line enhancer. These results were reinforced by visual results obtained from real ECoG data. The ASC showed that it could be an effective method for increasing signal to noise ratio in the presence of drifting sinusoidal noise, which is of significant interest for biomedical applications.
AB - This paper presents a method for filtering sinusoidal noise with a variable bandwidth filter that is capable of tracking a sinusoids drifting frequency. The method, which is based on the adaptive noise canceling (ANC) technique, will be referred to here as the adaptive sinusoid canceler (ASC). The ASC eliminates sinusoidal contamination by tracking its frequency and achieving a narrower bandwidth than typical notch filters. The detected frequency is used to digitally generate an internal reference instead of relying on an external one as ANC filters typically do. The filters bandwidth adjusts to achieve faster and more accurate convergence. In this paper, the focus of the discussion and the data is physiological signals, specifically electrocorticographic (ECoG) neural data contaminated with power line noise, but the presented technique could be applicable to other recordings as well. On simulated data, the ASC was able to reliably track the noises frequency, properly adjust its bandwidth, and outperform comparativemethods including standard notch filters and an adaptive line enhancer. These results were reinforced by visual results obtained from real ECoG data. The ASC showed that it could be an effective method for increasing signal to noise ratio in the presence of drifting sinusoidal noise, which is of significant interest for biomedical applications.
UR - http://www.scopus.com/inward/record.url?scp=84971673596&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2014.2375318
DO - 10.1109/JBHI.2014.2375318
M3 - Article
C2 - 25474814
AN - SCOPUS:84971673596
SN - 2168-2194
VL - 20
SP - 213
EP - 221
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 1
M1 - 6970762
ER -