An adaptive filter for the removal of drifting sinusoidal noise without a reference

John W. Kelly, Daniel P. Siewiorek, Asim Smailagic, Wei Wang

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

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.

Original languageEnglish
Article number6970762
Pages (from-to)213-221
Number of pages9
JournalIEEE Journal of Biomedical and Health Informatics
Volume20
Issue number1
DOIs
StatePublished - Jan 2016

Fingerprint

Dive into the research topics of 'An adaptive filter for the removal of drifting sinusoidal noise without a reference'. Together they form a unique fingerprint.

Cite this