Digital Processing Algorithms and Software Complex for Biomedical Signals Using Piecewise-Polynomial Methods

Biomedical signal processing is an important research area in modern healthcare, enabling the analysis of physiological processes and supporting the development of computer-assisted diagnostic systems. Electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG), and other biomedical signals frequently contain noise, artifacts, and nonlinear variations that complicate their interpretation. This article examines digital processing algorithms and software development approaches based on piecewise-polynomial methods for biomedical signal analysis. These methods approximate complex signals using polynomial functions defined over separate intervals, providing flexibility in representing local signal characteristics. The article discusses interpolation, smoothing, noise reduction, baseline correction, and feature extraction as potential applications. It also outlines the architecture of a software complex designed to integrate signal preprocessing, parameter selection, visualization, and quantitative evaluation. The proposed approach provides a methodological foundation for developing efficient and adaptable biomedical signal processing tools. Its practical performance should be evaluated using reference datasets and established signal quality metrics.