Linear and nonlinear heart rate variability indexes in clinical practice

Buccelletti Francesco, Bocci Maria Grazia, Gilardi Emanuele, Fiore Valentina, Calcinaro Sara, Fragnoli Chiara, Maviglia Riccardo, Franceschi Francesco, Buccelletti Francesco, Bocci Maria Grazia, Gilardi Emanuele, Fiore Valentina, Calcinaro Sara, Fragnoli Chiara, Maviglia Riccardo, Franceschi Francesco

Abstract

Biological organisms have intrinsic control systems that act in response to internal and external stimuli maintaining homeostasis. Human heart rate is not regular and varies in time and such variability, also known as heart rate variability (HRV), is not random. HRV depends upon organism's physiologic and/or pathologic state. Physicians are always interested in predicting patient's risk of developing major and life-threatening complications. Understanding biological signals behavior helps to characterize patient's state and might represent a step toward a better care. The main advantage of signals such as HRV indexes is that it can be calculated in real time in noninvasive manner, while all current biomarkers used in clinical practice are discrete and imply blood sample analysis. In this paper HRV linear and nonlinear indexes are reviewed and data from real patients are provided to show how these indexes might be used in clinical practice.

Figures

Figure 1
Figure 1
Series Standard Deviation (Frequentist Statistics). SDNN index displayed as mean (circles) and 95% confidential interval (Bars). Healthy subjects showed a higher degree of dispersion around the mean (higher variability) compared to critically ill patients, P = 0.10 using Mann-Witney U-test. ED: Emergency Department.
Figure 2
Figure 2
Fast Fourier Transform Analysis. Black dots represent healthy patients and empty squares ICU cases. Dashed and continuous lines reflect LF/HF ratio after adjusting for other clinical comorbidities along with 95% confidential intervals (curved lines). x-axis represents age in years. The two groups did not differ in term of LF/HF ratio (P = 0.82).
Figure 3
Figure 3
Detrended Fluctuation Analysis (DFA). Black dots represent healthy patients and empty squares ICU cases. Dashed and continuous lines reflect predicted values (adjusted for comorbidities) for the respective group along with 95% confidential intervals (curved lines). x-axis represents years. It is to be noted that DFA index was significantly different between the two groups even when adjusted for other comorbidities and age (P = 0.02). Age affects DFA index in both groups.

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