CardioRVAR: A New R Package and Shiny Application for the Evaluation of Closed-Loop Cardiovascular Interactions
Abstract
:Simple Summary
Abstract
1. Introduction
2. Materials and Methods
2.1. Vector Autoregressive Models
2.2. Causal Coherence and Noise Contribution
2.3. Trend Removal with the Discrete Wavelet Transform
2.4. CardioRVAR Workflow
- Select a data file with CardioRVARapp and upload it into the software structure;
- Resample the uploaded time series after selecting a certain frequency, if needed;
- Manually select from the estimated HR and BP recordings a specific time window of interest;
- Transform the model into the frequency domain;
- Extract instantaneous unidirectional interactions from this frequency-domain representation, given a specific zero-lag-interaction path already chosen by the user, and update the model with such interactions;
- Estimate the most important features of the model and then display and report them;
- Generate and report single-subject indices from the model, allowing the user to choose a method to estimate said indices.
2.5. Data Upload and Preprocessing
> library(CardioRVAR) |
> # Data is a list with elements Time, RR, and SBP: |
> Data <- ResampleData(Data, 4) # Interpolates data |
> IBI <- DetrendWithCutoff(Data$RR) # Detrends IBI signal |
> SBP <- DetrendWithCutoff(Data$SBP) # Detrends SBP signal |
> New_Data <- cbind(SBP = SBP, RR = IBI) |
> CheckStationarity(New_Data) # Checks stationarity of the data |
[1] TRUE |
> # Or alternatively: |
> Check_stationarity <- CheckStationarity(New_Data, verbose = TRUE) |
Time series are stationary |
2.6. Analysis of Cardiovascular Closed-Loop Interactions
> # Data represents a matrix with two interpolated time series, IBI and SBP. |
> Data[,“IBI”] = DetrendWithCutoff(Data[,“IBI”]) |
> Data[,“SBP”] = DetrendWithCutoff(Data[,“SBP”]) |
> # Both signals have been detrended with these commands. |
> CheckStationarity(Data) |
[1] TRUE |
> # A VAR model is estimated from the stationary time series and then validated: |
> model <- EstimateVAR(Data) |
> Check_residuals <- DiagnoseResiduals(model, verbose = TRUE) |
Model residuals are white noise processes |
> Check_stability <- DiagnoseStability(model, verbose = TRUE) |
The model is stable |
2.7. Analysis in the Frequency Domain
> freq_model <- ParamFreqModel(model). |
2.8. Assessment of the Transfer Functions
2.9. Assessment of the Noise Source Contribution and Causal Coherence
2.10. Evaluation of the Tool: Data Sources
3. Results and Discussion
3.1. Descriptive Study of Two Subjects
3.2. Comparison between Normotensive and Hypertensive Subjects
3.3. EUROBAVAR Analysis Results
3.4. Comparison with Other Works
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Subject A | Subject B | |||||
---|---|---|---|---|---|---|
Variable | Pre-Tilt Interval | Tilt Interval | Post-Tilt Interval | Pre-Tilt Interval | Tilt Interval | Post-Tilt Interval |
HR (bpm) | 48.27 | 54.03 | 49.42 | 78.42 | 109.74 | 69.75 |
SBP (mmHg) | 102.12 | 109.15 | 111.65 | 125.84 | 121.62 | 120.95 |
CT-HF αc (ms/mmHg) | 26.54 | 11.57 | 35.90 | 13.30 | N/A † | 13.60 |
WA-HF αc (ms/mmHg) | 24.13 | 9.05 | 31.56 | 9.54 | 1.76 | 12.81 |
MC-HF αc (ms/mmHg) | 27.34 | 12.50 | 36.33 | 13.10 | 1.78 | 16.33 |
CT-LF αc (ms/mmHg) | 6.95 | 1.74 | N/A † | 11.78 | 3.98 | 17.88 |
WA-LF αc (ms/mmHg) | 7.33 | 4.59 | 17.41 | 11.28 | 3.81 | 17.78 |
MC-LF αc (ms/mmHg) | 7.02 | 1.73 | 1.63 | 12.25 | 4.11 | 16.77 |
Position | Band | Estimate Type | Normotensive (n = 5) | Hypertensive (n = 7) | p Value |
---|---|---|---|---|---|
Supine rest | HF | Weighted-averaged | 9.02 ± 3.88 | 2.03 ± 0.45 | p < 0.01 |
Estimate at maximum coherence | 10.99 ± 4.14 | 3.10 ± 0.75 | p < 0.05 | ||
LF | Weighted-averaged | 5.94 ± 1.38 | 2.25 ± 0.39 | p = 0.054 | |
Estimate at maximum coherence | 6.19 ± 1.32 | 1.69 ± 0.37 | p < 0.05 | ||
Tilt | HF | Weighted-averaged | 4.34 ± 1.39 | 1.27 ± 0.29 | p = 0.091 |
Estimate at maximum coherence | 5.01 ± 1.95 | 1.46 ± 0.21 | p = 0.143 | ||
LF | Weighted-averaged | 4.90 ± 0.64 | 2.06 ± 0.22 | p < 0.01 | |
Estimate at maximum coherence | 4.69 ± 0.96 | 1.66 ± 0.23 | p < 0.05 |
Closed-Loop | Open-Loop (Type II) | Open-Loop (Type I) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Band | Method | Supine (ms/mmHg) | Standing (ms/mmHg) | p Value | Supine (ms/mmHg) | Standing (ms/mmHg) | p Value | Supine (ms/mmHg) | Standing (ms/mmHg) | p Value |
HF | Weighted average | 11.06 ± 2.46 | 3.54 ± 0.54 | p < 0.001 | 13.82 ± 2.92 | 5.03 ± 0.81 | p < 0.001 | 20.79 ± 3.74 | 7.82 ± 1.27 | p < 0.001 |
Maximum coherence | 13.03 ± 2.47 | 4.84 ± 0.79 | p < 0.001 | 16.07 ± 2.68 | 6.40 ± 1.07 | p < 0.001 | 17.40 ± 2.92 | 7.51 ± 1.38 | p < 0.001 | |
LF | Weighted average | 8.12 ± 1.72 | 4.12 ± 0.55 | p < 0.001 | 9.23 ± 2.25 | 5.12 ± 0.77 | p < 0.001 | 12.72 ± 2.75 | 7.12 ± 0.91 | p < 0.001 |
Maximum coherence | 7.92 ± 1.50 | 4.06 ± 0.54 | p < 0.01 | 10.48 ± 2.04 | 5.42 ± 0.68 | p < 0.01 | 12.43 ± 2.25 | 6.41 ± 0.77 | p < 0.01 |
Position | Band | (n.u.) | (n.u.) | p Value |
---|---|---|---|---|
Supine | HF | 0.23 ± 0.02 | 0.21 ± 0.02 | p = 0.644 |
LF | 0.19 ± 0.03 | 0.54 ± 0.04 | p < 0.001 | |
Standing | HF | 0.21 ± 0.03 | 0.19 ± 0.02 | p = 0.510 |
LF | 0.29 ± 0.04 | 0.32 ± 0.03 | p = 0.606 |
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Chao-Écija, A.; López-González, M.V.; Dawid-Milner, M.S. CardioRVAR: A New R Package and Shiny Application for the Evaluation of Closed-Loop Cardiovascular Interactions. Biology 2023, 12, 1438. https://doi.org/10.3390/biology12111438
Chao-Écija A, López-González MV, Dawid-Milner MS. CardioRVAR: A New R Package and Shiny Application for the Evaluation of Closed-Loop Cardiovascular Interactions. Biology. 2023; 12(11):1438. https://doi.org/10.3390/biology12111438
Chicago/Turabian StyleChao-Écija, Alvaro, Manuel Víctor López-González, and Marc Stefan Dawid-Milner. 2023. "CardioRVAR: A New R Package and Shiny Application for the Evaluation of Closed-Loop Cardiovascular Interactions" Biology 12, no. 11: 1438. https://doi.org/10.3390/biology12111438