Jump to main content
Jump to site search

Baseline Drift Detrending Techniques for Fast Scan Cyclic Voltammetry


Fast scan cyclic voltammetry (FSCV) has been commonly used to measure extracellular neurotransmitter concentrations in the brain. Due to the unstable nature of the background currents inherent in FSCV measurements, analysis of FSCV data is limited to very short amounts of time using traditional background subtraction. In this paper, we propose the use of a zero-phase high pass filter (HPF) as the means to remove the background drift. Instead of the traditional method of low pass filtering across voltammograms to increase signal to noise ratio, a HPF with a low cutoff frequency was applied to temporal data set at each voltage point to remove background drift. As a result, the HPF utilizing cutoff frequencies between 0.001 Hz to 0.01 Hz could be effectively used to a set of FSCV data for removing drifting patterns while preserving the temporal kinetics of the phasic dopamine response recorded in vivo. In addition, compared to a drift removal method using principal component analysis, this was found to be significantly more effective in reducing drift (unpaired t-test p<0.0001, t=10.88) when applied to data collected from tris buffer over 24 hours although a drift removal method using principal component analysis also showed the effective background drift reduction. The HPF was also applied to 5 hours of FSCV in vivo data. Electrically evoked dopamine peaks, observed in the nucleus accumbens, were clearly visible even without background subtraction. This technique provides a new, simple, and yet robust, approach to analyse FSCV data with unstable background.

Back to tab navigation

Publication details

The article was accepted on 26 Sep 2017 and first published on 27 Sep 2017

Article type: Paper
DOI: 10.1039/C7AN01465A
Citation: Analyst, 2017, Accepted Manuscript
  •   Request permissions

    Baseline Drift Detrending Techniques for Fast Scan Cyclic Voltammetry

    M. DeWaele, Y. Oh, C. Park, Y. M. Kang, H. Shin, C. Blaha, K. E. Bennet, I. Y. Kim, K. H. Lee and D. P. Jang, Analyst, 2017, Accepted Manuscript , DOI: 10.1039/C7AN01465A

Search articles by author