RT Journal Article
A1 Dai, Weiwei
A1 Selesnick, Ivan
A1 Rizzo, John-Ross
A1 Rucker, Janet
A1 Hudson, Todd
T1 A nonlinear generalization of the Savitzky-Golay filter and the quantitative analysis of saccades
JF Journal of Vision
JO JOV
YR 2017
DO 10.1167/17.9.10
JF Journal of Vision
VO 17
IS 9
SP 10
OP 10
SN 1534-7362
AB The Savitzky-Golay (SG) filter is widely used to smooth and differentiate time series, especially biomedical data. However, time series that exhibit abrupt departures from their typical trends, such as sharp waves or steps, which are of physiological interest, tend to be oversmoothed by the SG filter. Hence, the SG filter tends to systematically underestimate physiological parameters in certain situations. This article proposes a generalization of the SG filter to more accurately track abrupt deviations in time series, leading to more accurate parameter estimates (e.g., peak velocity of saccadic eye movements). The proposed filtering methodology models a time series as the sum of two component time series: a low-frequency time series for which the conventional SG filter is well suited, and a second time series that exhibits instantaneous deviations (e.g., sharp waves, steps, or more generally, discontinuities in a higher order derivative). The generalized SG filter is then applied to the quantitative analysis of saccadic eye movements. It is demonstrated that (a) the conventional SG filter underestimates the peak velocity of saccades, especially those of small amplitude, and (b) the generalized SG filter estimates peak saccadic velocity more accurately than the conventional filter.
RD 10/21/2019
UL https://doi.org/10.1167/17.9.10