CardioRVAR: A New R Package and Shiny Application for the Evaluation of Closed-Loop Cardiovascular Interactions

Figure 1. Trend isolation process through a six-level decomposition with the MODWT. The coefficients shown belong to the original signal and its smoothed representation through approximation coefficients (black), as well as to each decomposition level from D1 to D6, which hold the detail coefficients obtained from the analysis (red).

Figure 2. Trend isolation process through the MODWT with an additional border handling strategy applied to the transform. The handling strategy extends the original IBI signal and creates an additional number of coefficients (blue).

Figure 3. Interface to upload and window cardiovascular data. A csv file containing the necessary data is uploaded and interpolated up to a sample frequency of 4 Hz. A time window is selected, and mean HR and SBP values from this window are reported. The data were obtained from a subject during a head-up tilt session.

Figure 4. Time-domain and frequency-domain changes in HRV due to the preprocessing algorithms available in CardioRVAR. (A) Non-detrended signal, with a sample frequency of 4 Hz, and its frequency-domain non-parametric and parametric spectra, with a significant very-low-frequency component. (B) Detrended signal after selecting a reference frequency of 0.04 Hz (cutoff: 0.03125 Hz, blue mark), accompanied by its non-parametric and parametric spectra, showing a mitigated very-low-frequency component. (C) Detrended signal after selecting a reference frequency of 0.07 Hz (cutoff: 0.0625 Hz, blue mark), with spectral densities showing a more mitigated very-low-frequency component, while also affecting part of the LF component. Yellow areas indicate ranges of possible reference frequencies associated with the last decomposition level, used to identify the cutoff frequency (blue marks) in each case. Green marks indicate cutoff frequencies (0.0625 Hz, 0.125 Hz) associated with the previous decomposition level. Red areas indicate frequency components below 0.04 Hz.

Figure 5. Relative errors computed for each frequency between the frequency-domain non-parametric spectra of a raw IBI signal and its detrended version using the MODWT-based detrending algorithm with (A) the Haar wavelet and (B) the Daubechies 8 wavelet. Vertical lines indicate frequencies 0.04 Hz and 0.15 Hz.

Figure 6. Transfer functions reported by the software from patient during (A) head-up tilt and (B) post-tilt recovery. From left to right: closed-loop transfer function without zero-lagged interactions, closed-loop transfer function with zero-lagged interactions, and open-loop transfer function. Color code: LF band (green), HF band (yellow).

Figure 7. Percentage of noise source contribution computed from a patient to the variability of (A) IBI before head-up tilt, (B) SBP before head-up tilt, (C) IBI during head-up tilt, and (D) SBP during head-up tilt. Color code: IBI noise (white), SBP noise (blue).

Figure 8. Heart rate (red) and systolic blood pressure (blue) recordings obtained from (A) subject A, a healthy patient who exhibited a weak response during head-up tilt, and (B) subject B, who suffered from Postural Orthostatic Tachycardia Syndrome induced by long-COVID.

Figure 9. Individual closed-loop BRS estimates computed through CardioRVARapp from the EUROBAVAR A series of subjects, and their distributions, obtained in (A) supine position, through the maximum coherence strategy; (B) supine position, through the coherence-thresholding strategy; (C) standing position, through the maximum coherence strategy; and (D) standing position, through the coherence-thresholding strategy. Blue arrows indicate subjects with missing estimates due to the coherence-thresholding strategy. Color code: HF band (black), LF band (white).

Figure 10. Individual closed-loop BRS estimates computed through CardioRVARapp from the EUROBAVAR B series of subjects, and their distributions, obtained in (A) supine position, through the maximum coherence strategy; (B) supine position, through the coherence-thresholding strategy; (C) standing position, through the maximum coherence strategy; and (D) standing position, through the coherence-thresholding strategy. Red arrows indicate baroreflex-impaired subjects. Blue arrows indicate non-impaired subjects with missing estimates due to the coherence-thresholding strategy. Purple arrows indicate baroreflex-impaired subjects with missing estimates due to the coherence-thresholding strategy. Color code: HF band (black), LF band (white).

Figure 11. Baroreflex sensitivity changes in closed-loop estimates computed by CardioRVARapp from EUROBAVAR during supine and standing positions in HF and LF bands, excluding the two impaired ones (n = 19), through (A) Gaussian-weighting strategy and (B) maximum coherence strategy. Significance: p < 0.01 (**), p < 0.001 (***).

Figure 12. Causal coherence flows from (A) HF band from IBI to SBP (dark green) and vice versa (light green) at both supine (left) and standing (right) positions, and (B) HF band from IBI to SBP (dark green) and vice versa (light green) at both supine (left) and standing (right) positions. The two impaired subjects were excluded from the analysis (n = 19). Significance: p < 0.001 (***).