  This paper introduces an adaptive filtering process based on shrinking
wavelet coefficients from the corresponding signal wavelet representation. The
filtering procedure considers a threshold method determined by an iterative
algorithm inspired by the control charts application, which is a tool of the
statistical process control (SPC). The proposed method, called SpcShrink, is
able to discriminate wavelet coefficients that significantly represent the
signal of interest. The SpcShrink is algorithmically presented and numerically
evaluated according to Monte Carlo simulations. Two empirical applications to
real biomedical data filtering are also included and discussed. The SpcShrink
shows superior performance when compared with competing algorithms.
