Correlations between Indices of the Heart Rate Variability and Parameters of Ongoing EEG in Patients Suffering from Chronic Renal Pathology
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We examined correlations between indices of the heart rate variability (HRV) measured according to the cardiointervalogram (CIG) and parameters of the main rhythms of EEG recorded in a parallel mode in men suffering from chronic pyelonephritis (in the remission phase). We obtained a few equations of multiple regression that reflect interrelations between the absolute and relative (normalized) spectral and time-domain indices of CIG, on the one hand, and the amplitude/frequency and spectral parameters of EEG, on the other hand. According to values of the coefficient of canonical correlation with EEG parameters, CIG indices formed the following sequence: amplitude of the mode (R = 0.72), spectral power density (SPD) of the low-frequency (LF) component (R = 0.66), pNN50 (R = 0.65), mode (R = 0.64), SDNN (R = 0.63), SPD of the very low-frequency (VLF) component (R = 0.625), SPD of the high-frequency (HF) component (R = 0.55), RMSSD (R = 0.545), SPD of the ultralow-frequency (ULF) component (R = 0.455), and variation range (R = 0.38). Relative SPDs of the CIG components, similarly to LFnu, showed weaker correlations with EEG parameters (R = 0.535-0.42 and R = 0.42), while the ratio between the LF and HF powers demonstrated comparatively strong correlation (R = 0.56). The Baevsky stress index showed maximum correlation with EEG parameters (R = 0.80). According to the factor loadings, the absolute SPD of the α rhythm in leads O1 (r * = -0.51), Р3 (r* = -0.35) і С4 (r* = -0.29), that of the δ rhythm in leads Fp1 (r* = -0.41), O1 (r* = -0.40), T5 (r* = -0,39), F7 (r* = -0.39), and T6 (r* = -0.35), that of the β rhythm in locus О1 (r* = -0.34), relative SPD of the θ rhythm in loci F8 (r* = 0.38), Fр1 (r* = 0.32), and F4 (r* = 0.28), index of the θ rhythm (r* = 0.47), and also the modal β rhythm frequency (r * = 0.43) exert most significant influences on variability of the heart rhythm.
Keywordsheart rate variability (HRV) cardiointervalogram (CIG) EEG autonomic regulation of the functions interrelations between the CIG and EEG parameters canonical correlation regression models chronic renal pathologies
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