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Methodological framework for estimating the correlation dimension in HRV signals.

Bolea J, Laguna P, Remartínez JM, Rovira E, Navarro A, Bailón R - Comput Math Methods Med (2014)

Bottom Line: Each approach for slope estimation leads to a correlation dimension estimate, called D₂, D(2(⊥)), and D(2(max)).D₂ and D(2(max)) estimate the theoretical value of correlation dimension for the Lorenz attractor with relative error of 4%, and D(2(⊥)) with 1%.D₂ keeps the 81% of accuracy previously described in the literature while D(2(⊥)) and D(2(max)) approaches reach 91% of accuracy in the same database.

View Article: PubMed Central - PubMed

Affiliation: Communications Technology Group (GTC), Aragón Institute for Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain ; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 50018 Zaragoza, Spain.

ABSTRACT
This paper presents a methodological framework for robust estimation of the correlation dimension in HRV signals. It includes (i) a fast algorithm for on-line computation of correlation sums; (ii) log-log curves fitting to a sigmoidal function for robust maximum slope estimation discarding the estimation according to fitting requirements; (iii) three different approaches for linear region slope estimation based on latter point; and (iv) exponential fitting for robust estimation of saturation level of slope series with increasing embedded dimension to finally obtain the correlation dimension estimate. Each approach for slope estimation leads to a correlation dimension estimate, called D₂, D(2(⊥)), and D(2(max)). D₂ and D(2(max)) estimate the theoretical value of correlation dimension for the Lorenz attractor with relative error of 4%, and D(2(⊥)) with 1%. The three approaches are applied to HRV signals of pregnant women before spinal anesthesia for cesarean delivery in order to identify patients at risk for hypotension. D₂ keeps the 81% of accuracy previously described in the literature while D(2(⊥)) and D(2(max)) approaches reach 91% of accuracy in the same database.

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The left panel shows two RR intervals, one corresponding to a patient who developed a hypotension event (Hyp) and the other to one who did not (NoHyp); the right panel shows the  estimation using the perpendicular points in the log-log curves.
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fig7: The left panel shows two RR intervals, one corresponding to a patient who developed a hypotension event (Hyp) and the other to one who did not (NoHyp); the right panel shows the estimation using the perpendicular points in the log-log curves.

Mentions: Thus, the D2 estimate in HRV signals may shed light on the degree of complexity of the ANS or how many degrees of freedom it has. The group of women (Hyp) suffering hypotension events occurring during the surgery of a programmed cesarean section under spinal anesthesia showed higher D2 values than the group who did not (NoHyp), in the lateral decubitus position. As an example Figure 7 shows one patient of each group and the estimate. All the proposed correlation dimension estimates not only maintain the accuracy obtained in [10], they also increase it. Predicting hypotension is a challenge since it occurs in the 60% of the cases producing fetal stress [28]. If the goal is to predict those who are going to suffer hypotension, then the estimates that performed 100% of specificity will be selected, being D2 [10], , and . Otherwise, if the goal is to use prophylaxis in the less number of patients to prevent hypotension, then the estimates that performed 100% of sensitivity will be chosen, and in this case it is . The effect of prophyilaxis on patients who finally are not going to suffer a hypotension event and the relation with fetal stress needs further studies.


Methodological framework for estimating the correlation dimension in HRV signals.

Bolea J, Laguna P, Remartínez JM, Rovira E, Navarro A, Bailón R - Comput Math Methods Med (2014)

The left panel shows two RR intervals, one corresponding to a patient who developed a hypotension event (Hyp) and the other to one who did not (NoHyp); the right panel shows the  estimation using the perpendicular points in the log-log curves.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3926396&req=5

fig7: The left panel shows two RR intervals, one corresponding to a patient who developed a hypotension event (Hyp) and the other to one who did not (NoHyp); the right panel shows the estimation using the perpendicular points in the log-log curves.
Mentions: Thus, the D2 estimate in HRV signals may shed light on the degree of complexity of the ANS or how many degrees of freedom it has. The group of women (Hyp) suffering hypotension events occurring during the surgery of a programmed cesarean section under spinal anesthesia showed higher D2 values than the group who did not (NoHyp), in the lateral decubitus position. As an example Figure 7 shows one patient of each group and the estimate. All the proposed correlation dimension estimates not only maintain the accuracy obtained in [10], they also increase it. Predicting hypotension is a challenge since it occurs in the 60% of the cases producing fetal stress [28]. If the goal is to predict those who are going to suffer hypotension, then the estimates that performed 100% of specificity will be selected, being D2 [10], , and . Otherwise, if the goal is to use prophylaxis in the less number of patients to prevent hypotension, then the estimates that performed 100% of sensitivity will be chosen, and in this case it is . The effect of prophyilaxis on patients who finally are not going to suffer a hypotension event and the relation with fetal stress needs further studies.

Bottom Line: Each approach for slope estimation leads to a correlation dimension estimate, called D₂, D(2(⊥)), and D(2(max)).D₂ and D(2(max)) estimate the theoretical value of correlation dimension for the Lorenz attractor with relative error of 4%, and D(2(⊥)) with 1%.D₂ keeps the 81% of accuracy previously described in the literature while D(2(⊥)) and D(2(max)) approaches reach 91% of accuracy in the same database.

View Article: PubMed Central - PubMed

Affiliation: Communications Technology Group (GTC), Aragón Institute for Engineering Research (I3A), IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain ; CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), 50018 Zaragoza, Spain.

ABSTRACT
This paper presents a methodological framework for robust estimation of the correlation dimension in HRV signals. It includes (i) a fast algorithm for on-line computation of correlation sums; (ii) log-log curves fitting to a sigmoidal function for robust maximum slope estimation discarding the estimation according to fitting requirements; (iii) three different approaches for linear region slope estimation based on latter point; and (iv) exponential fitting for robust estimation of saturation level of slope series with increasing embedded dimension to finally obtain the correlation dimension estimate. Each approach for slope estimation leads to a correlation dimension estimate, called D₂, D(2(⊥)), and D(2(max)). D₂ and D(2(max)) estimate the theoretical value of correlation dimension for the Lorenz attractor with relative error of 4%, and D(2(⊥)) with 1%. The three approaches are applied to HRV signals of pregnant women before spinal anesthesia for cesarean delivery in order to identify patients at risk for hypotension. D₂ keeps the 81% of accuracy previously described in the literature while D(2(⊥)) and D(2(max)) approaches reach 91% of accuracy in the same database.

Show MeSH
Related in: MedlinePlus