Application of Approximation Methods to Identify Sinusoidal Chronostructures in Healthy Individuals and Patients with Cardiovascular Pathology

This study presents a mathematical and computational approach for identifying sinusoidal chronostructures in physiological data collected from healthy individuals and patients with cardiovascular pathology.Using a nonlinear least squares approximation based on the Levenberg-Marquardt optimization algorithm, it is estimated rhythm parameters such as mesor, amplitude, acrophase, and period for various biological signals, including blood pressure, heart rate, glucose levels, and blood oxygen saturation.The classification of biological rhythms followed the international chronobiological standard into ultradian, circadian, and infradian categories.A single patient's clinical data over three days was analyzed as a test case to validate the method.The results confirmed the presence of circadian rhythms in most features and demonstrated the reliability and feasibility of the proposed method for detecting rhythmicity in biomedical data.This approach offers potential for broader chronobiological diagnostics and rhythm-based assessment of pathological conditions.