lowpassFilter-package {lowpassFilter}

Lowpass Filtering

Description

Creates lowpass filters and offers further functionalities around them. Lowpass filters are commonly used in ion channel recordings.

Details

The main function of this package is lowpassFilter which creates lowpass filters, currently only Bessel filters are supported. randomGeneration and randomGenerationMA allow to generate random numbers that are filtered, i.e. follow a model for ion channel recordings, see (Pein et al., 2018, 2020). getConvolution, getConvolutionJump, and getConvolutionPeak allow to compute the convolution of a signal with the kernel of a lowpass filter.

References

Pein, F., Bartsch, A., Steinem, C., and Munk, A. (2020) Heterogeneous idealization of ion channel recordings - Open channel noise. Submitted.

Pein, F., Tecuapetla-Gómez, I., Schütte, O., Steinem, C., Munk, A. (2018) Fully-automatic multiresolution idealization for filtered ion channel recordings: flickering event detection. IEEE Trans. Nanobioscience, 17(3):300-320.

Pein, F. (2017) Heterogeneous Multiscale Change-Point Inference and its Application to Ion Channel Recordings. PhD thesis, Georg-August-Universität Göttingen. http://hdl.handle.net/11858/00-1735-0000-002E-E34A-7.

Hotz, T., Schütte, O., Sieling, H., Polupanow, T., Diederichsen, U., Steinem, C., and Munk, A. (2013) Idealizing ion channel recordings by a jump segmentation multiresolution filter. IEEE Trans. Nanobioscience, 12(4):376-386.

See Also

lowpassFilter, randomGeneration, randomGenerationMA, getConvolution, getConvolutionJump, getConvolutionPeak

Examples

# creates a lowpass filter
filter <- lowpassFilter(type = "bessel", param = list(pole = 4, cutoff = 0.1), sr = 1e4)
time <- 1:4000 / filter$sr

# creates a piecewise constant signal with a single peak
stepfun <- getSignalPeak(time, cp1 = 0.2, cp2 = 0.2 + 3 / filter$sr, 
                         value = 20, leftValue = 40, rightValue = 40)

# computes the convolution of the signal with the kernel of the lowpass filter
signal <- getConvolutionPeak(time, cp1 = 0.2, cp2 = 0.2 + 3 / filter$sr, 
                             value = 20, leftValue = 40, rightValue = 40,
                             filter = filter)

# generates random numbers that are filtered                             
data <- randomGenerationMA(n = 4000, filter = filter, signal = signal, noise = 1.4)

# generated data
plot(time, data, pch = 16)

# zoom into the single peak
plot(time, data, pch = 16, xlim = c(0.199, 0.202), ylim = c(19, 45))
lines(time, stepfun, col = "blue", type = "s", lwd = 2)
lines(time, signal, col = "red", lwd = 2)

# use of data randomGeneration instead
data <- randomGeneration(n = 4000, filter = filter, signal = signal, noise = 1.4)

# similar result
plot(time, data, pch = 16, xlim = c(0.199, 0.202), ylim = c(19, 45))
lines(time, stepfun, col = "blue", type = "s", lwd = 2)
lines(time, signal, col = "red", lwd = 2)