Separating low- and high-frequency components of rheographic signals enables arterial pressure waveform reconstruction and improves the accuracy of non-invasive pressure estimation.
A novel algorithmic approach for processing rheographic signals enables the reconstruction of arterial pressure waveforms, improving the accuracy of non-invasive central and peripheral pressure estimation.
The article presents an algorithmic approach to the digital processing of rheographic signals for non-invasive arterial pressure assessment. The proposed method is based on a comprehensive analysis of the frequency structure of the rheosignal, enabling the separation of its low-frequency (first harmonic) and high-frequency components, which correspond to the global hemodynamic behavior and the local vascular properties, respectively. The study describes algorithms for signal filtering, baseline drift correction, segmentation, valid period selection, normalization, and averaging. It is demonstrated that separate processing of the low- and high-frequency components enhances the stability of the signal’s morphological features and enables the reconstruction of the arterial pressure waveform in the time domain. Calibration is performed using reference systolic and diastolic pressure values obtained by a standard measurement technique. The proposed approach provides a unified algorithmic structure for the assessment of both central and peripheral arterial pressure based on rheographic signals, thereby improving the accuracy and reproducibility of non-invasive pressure estimation.
Zarubin et al. (2026) studied this question. Separating low- and high-frequency components of rheographic signals enables arterial pressure waveform reconstruction and improves the accuracy of non-invasive pressure estimation.