Key result
Complex correlation measure outperforms SD1 and SD2 in detecting heart rate variability temporal changes.
Why the study?
Heart rate variability analysis is important but standard measurement methods remain complex and fraught with pitfalls, motivating exploration of nonlinear techniques like the Poincaré plot.
HRV analysis supports autonomic research; Level 5 evidence leaves open routine clinical adoption.
Heart is the most important part in the body of living organisms. It affects and is affected by any factor in the body. Therefore, it is a good detector for all conditions in the body. Heart signal is a non-stationary signal; thus, it is utmost important to study the variability of heart signal. The Heart Rate Variability (HRV) has attracted considerable attention in psychology, medicine and has become important dependent measure in psychophysiology and behavioral medicine. The standards of measurements, physiological interpretation and clinical use for HRV that are most often used were described in many researcher papers, however, remain complex issues are fraught with pitfalls. This paper presents one of the nonlinear techniques to analyze HRV. It discusses many points like, what Poincaré plot is and how Poincaré plot works; also, Poincaré plot's merits especially in HRV. Besides, it discusses the limitation of Poincaré cause of standard deviation SD1, SD2 and how to overcome this limitation by using complex correlation measure (CCM). The CCM is most sensitive to changes in temporal structure of the Poincaré plot as compared toSD1 and SD2.
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Tayel et al. (2015) conducted a review in Heart Rate Variability. Complex correlation measure (CCM) vs. Standard deviation measures (SD1, SD2) was evaluated. The complex correlation measure (CCM) is more sensitive to changes in the temporal structure of the Poincaré plot compared to standard deviation metrics SD1 and SD2 for analyzing heart rate variability.
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