The wavelet denoising algorithm works by considering data points as a complex signal formed from numerous high and low frequencies. The algorithm consists of several steps (called ‘levels’ in the wavelet literature). For the first level, the signal is decomposed into two parts, denoted the scaling coefficients and detail (or wavelet) coefficients. The detail coefficients provide an indication of the high frequency variation of the signal, and hence the noise. Several levels of signal decomposition are performed (five levels in KAPPA-Automate), with each set of scaling/detail coefficients created from the scaling coefficients at the previous level. A noise threshold is applied to the detail coefficients at every level to remove the noise. After thresholding, the process is simply reversed, with the scaling and detail coefficients recombined to form a denoised signal. This signal is smoother than the initial one and with the characteristic features preserved.

During the decimation process, it is possible to specify the number of points to retain from the de-noised data. This is set by specifying the “Delta-T max” and/or “Delta-Y max” criteria: one point every Delta-t max, with Delta-y value less than Delta-y max;