Digital processing of biomedical signalsin haar's part-wavelet models

This article is devoted to the construction of Haar's fragmentary-polynomial wavelet models, which are important in the digital processing of electroencephalographic signals in biomedicine. These models were built by using Haar's piece-constant, piece-line, and piece-square wavelets. Haar's fragmentary-polynomial wavelet models have high accuracy in the digital processing of biomedical signals. The use of wavelet modifications for the analysis of electroencephalographic signals allows to expand the volume of useful data obtained during the digital processing of data recorded from patients during clinical or physiological studies. As an example in the study, the first experimental data of the biomedical electroencephalographic signal were obtained, and on the basis of this data, Haar's fragment-wavelet models were constructed and their errors were evaluated.