Jerzy Sacha

An introduction to heart rate variability: methodological considerations and clinical applications.

Heart rate variability (HRV), the beat-to-beat variation in either heart rate or the duration of the R-R interval, has become a popular clinical and investigational tool (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996; Billman, 2011). Indeed, the term “heart rate variability” yields nearly 18,000 “hits” when placed in the pubmed search engine. These temporal fluctuations in heart rate exhibit a marked synchrony with respiration (increasing during inspiration and decreasing during expiration—the so called respiratory sinus arrhythmia) and are widely believed to reflect changes in cardiac autonomic regulation (Billman, 2011). Although the exact contributions of the parasympathetic and the sympathetic divisions of the autonomic nervous system to this variability are controversial and remain the subject of active investigation and debate, a number of time and frequency domain techniques have been developed to provide insight into cardiac autonomic regulation in both health and disease (Billman, 2011). It is the purpose of this book to provide a comprehensive assessment of the strengths and limitations of HRV techniques. Particular emphasis will be placed on the application of HRV techniques in the clinic and on the interaction between prevailing heart rate and HRV. This book contains both state-of-the art review and original research articles that have been grouped into two main sections: Methodological Considerations and Clinical Application. A brief summary of the chapters contained in each section follows below.

How to strengthen or weaken the HRV dependence on heart rate — Description of the method and its perspectives

a Department of Cardiology, Regional Medical Center, Opole, Poland b Institute of Physics, Wroclaw University of Technology, Wroclaw, Poland c Instytut Fizyki imienia Mariana Smoluchowskiego and Mark Kac Complex Systems Research Center, Uniwersytet Jagiellonski, ulica Reymonta 4, PL-30-059 Krakow, Poland d 1. Medizinische Klinik und Deutsches Herzzentrum München der Technischen Universität München, Munich, Germany e Department of Cardiology-Intensive Therapy, University School of Medicine, Poznan, Poland f Institute of Physics, University of Zielona Gora, Zielona Gora, Poland

Why should one normalize heart rate variability with respect to average heart rate

Heart rate variability (HRV) is a recognized risk factor in many disease states (Bravi et al., 2011; Sacha et al., 2013a). However, HRV is significantly correlated with an average heart rate (HR), and this association is both physiologically and mathematically determined. The physiological determination comes from the autonomic nervous system activity (Task Force of the European Society of Cardiology, and the North American Society of Pacing and Electrophysiology, 1996), but the mathematical one is caused by the non-linear (inverse) relationship between R-R interval and HR (Sacha and Pluta, 2005a,b, 2008). HRV may be estimated by using R-R interval (the most frequent method) or HR signals, yet, they both do not yield the same results since they are inversely related with each other—indeed, the analyses are mathematically biased (Sacha and Pluta, 2005a,b). If one uses R-R intervals, the same changes of HR cause much higher fluctuations of R-R intervals for the slow average HR than for the fast one (Figure ​(Figure1A).1A). Conversely, if one employs HR signals, the same changes of R-R intervals cause much higher fluctuations of HR for the fast than slow average HR (Figure ​(Figure1B).1B). Consequently, due to these mathematical reasons, HRV estimated from R-R intervals should negatively correlate with average HR (or positively with average R-R interval), but HRV estimated from HR signals should be positively correlated with average HR (or negatively with average R-R interval) (Sacha and Pluta, 2005a,b). Moreover, due to the inverse relationship between R-R interval and HR, there is a possibility that a given patient may have higher HRV than another in terms of R-R intervals and lower HRV in terms of HRs—Figure 1C explains such a case (Sacha and Pluta, 2005a). Figure 1 (A)The non-linear (mathematical) relationship between R-R interval and heart rate is depicted. One can see that the oscillations of a slow average heart rate (x-axis, dark gray area) result in much greater oscillations of RR intervals (y-axis, dark gray ... Another mathematical problem concerning the association between HRV and HR is presented in Figure ​Figure1D.1D. One can see that the fluctuations of R-R intervals may be potentially very high for slow average HR, however, the same fluctuations are not possible for fast average HR, since the R-R intervals should have become negative. The same problem can be met if one calculates HRV from HR signals, i.e., the average HR of 80 bpm may potentially fluctuate between 30 and 130 bpm (i.e., the fluctuation amplitude equals 100 bpm), however, such fluctuations are not possible for the average HR of 40 bpm, since the heart rhythm must have fluctuated between −10 and 90 bpm. Due to the above facts, the standard HRV analysis is mathematically biased, particularly if patients differ in terms of their average HRs. The only way to overcome it is to calculate HRV with respect to the average value, i.e., to normalize the fluctuations with respect to the mean (Sacha and Pluta, 2005a,b, 2008). One can do that by division of the signal by the average R-R interval in the case of R-R interval signal, or by the average HR in the case of HR signal. Moreover, this way the same results are obtained no matter if one calculates HRV from R-R intervals or HRs (Sacha and Pluta, 2005a). Such an approach enables to explore the HR contribution to the physiological and clinical significance of HRV (Billman, 2013; Sacha et al., 2013a). Recently, this approach has been further developed to enhance or completely remove the HR influence (even physiological one) on HRV, what turned out to provide valuable information on cardiac and non-cardiac prognosis in patients after myocardial infarction—the details have been published elsewhere (Sacha et al., 2013a,b,c). To conclude, HRV is significantly associated with HR, which is caused by both physiological and mathematical phenomena, however, by a simple mathematical modification one may exclude mathematical bias and explore a real clinical value of HR and its variability.

How to select patients who will not benefit from ICD therapy by using heart rate and its variability

How to select patients who will not benefit from ICD therapy by using heart rate and its variability? Jerzy Sacha ⁎, Szymon Barabach , Gabriela Statkiewicz-Barabach , Krzysztof Sacha , Alexander Muller , Jaroslaw Piskorski , Petra Barthel , Georg Schmidt d,1 a Department of Cardiology, Regional Medical Center, Opole, Poland b Institute of Physics, Wroclaw University of Technology, Wroclaw, Poland c Instytut Fizyki imienia Mariana Smoluchowskiego and Mark Kac Complex Systems Research Center, Uniwersytet Jagiellonski, ulica Reymonta 4, PL-30-059 Krakow, Poland d 1. Medizinische Klinik und Deutsches Herzzentrum Munchen der Technischen Universitat Munchen, Munich, Germany e Department of Cardiology-Intensive Therapy, University School of Medicine, Poznan, Poland f Institute of Physics, University of Zielona Gora, Zielona Gora, Poland

Interaction Between Heart Rate Variability and Heart Rate in Pediatric Population

Background: Heart rate variability (HRV) is primarily heart rate (HR) dependent, and therefore, different HR may exert different impact on HRV. The objectives of the study were to evaluate the effect of HR on HRV in children and to determine whether HRV indices normalized to HR are sex- and age-related. Methods: Short-term ECG recordings were performed in 346 healthy children. Standard time and frequency domain HRV parameters and HR were analyzed in four age subgroups (6–7, 8–9, 10–11, and 12–13 years old). To investigate the HR impact on HRV, standard HRV parameters were normalized to prevailing HR. Results: Standard HRV measures did not differ between age subgroups, however, HR significantly decreased with subjects age and turned out to be the strongest determinant of HRV. The normalization of HRV to prevailing HR allowed to show that sex-related differences in standard HRV resulted from differences in HR between boys and girls. The normalized HRV significantly decreased with age—before the normalization this effect was masked by age-related HR alterations. Conclusions: HR significantly impacts HRV in pediatric population and turns out to be the strongest determinant of all standard HRV indices. The differences in standard HRV between boys and girls result from differences in their HR. The normalized HRV is decreasing with age in healthy children and it is accompanied by the reduction of HR—as a net result, the standard HRV is constant in children at different ages. This may reflect the maturation of the autonomic nervous system.

Heart Rate and Respiratory Rate Influence on Heart Rate Variability Repeatability: Effects of the Correction for the Prevailing Heart Rate

Background: Since heart rate variability (HRV) is associated with average heart rate (HR) and respiratory rate (RespRate), alterations in these parameters may impose changes in HRV. Hence the repeatability of HRV measurements may be affected by differences in HR and RespRate. The study aimed to evaluate HRV repeatability and its association with changes in HR and RespRate. Methods: Forty healthy volunteers underwent two ECG examinations 7 days apart. Standard HRV indices were calculated from 5-min ECG recordings. The ECG-derived respiration signal was estimated to assess RespRate. To investigate HR impact on HRV, HRV parameters were corrected for prevailing HR. Results: Differences in HRV parameters between the measurements were associated with the changes in HR and RespRate. However, in multiple regression analysis only HR alteration proved to be independent determinant of the HRV differences—every change in HR by 1 bpm changed HRV values by 16.5% on average. After overall removal of HR impact on HRV, coefficients of variation of the HRV parameters significantly dropped on average by 26.8% (p < 0.001), i.e., by the same extent HRV reproducibility improved. Additionally, the HRV correction for HR decreased association between RespRate and HRV. Conclusions: In stable conditions, HR but not RespRate is the most powerful factor determining HRV reproducibility and even a minimal change of HR may considerably alter HRV. However, the removal of HR impact may significantly improve HRV repeatability. The association between HRV and RespRate seems to be, at least in part, HR dependent.

Gender differences in the interaction between heart rate and its variability — How to use it to improve the prognostic power of heart rate variability

a Department of Cardiology, Regional Medical Center, Opole, Poland b Institute of Physics, Wroclaw University of Technology, Wroclaw, Poland c Instytut Fizyki imienia Mariana Smoluchowskiego and Mark Kac Complex Systems Research Center, Uniwersytet Jagiellonski, ulica Reymonta 4, PL-30-059 Krakow, Poland d 1.Medizinische Klinik und Deutsches Herzzentrum Munchen der Technischen Universitat Munchen, Munich, Germany e Institute of Physics, University of Zielona Gora, Zielona Gora, Poland

Short-term deceleration capacity reveals higher reproducibility than spectral heart rate variability indices during self-monitoring at home.

rate variability indices during self-monitoring at home Jerzy Sacha ⁎, Jacek Sobon , Krzysztof Sacha , Alexander Muller , Georg Schmidt d a Department of Cardiology, Regional Medical Center, Opole, Poland b Faculty of Physical Education and Physiotherapy, Opole University of Technology, Opole, Poland c Uniwersytet Jagiellonski, ulica Reymonta 4, PL-30-059 Krakow, Poland d 1. Medizinische Klinik und Deutsches Herzzentrum Munchen der Technischen Universitat Munchen, Munchen, Germany

Is It Time to Begin a Public Campaign Concerning Frailty and Pre-frailty? A Review Article

Frailty is a state that encompasses losses in physical, psychological or social domains. Therefore, frail people demonstrate a reduced potential to manage external stressors and to respond to life incidents. Consequently, such persons are prone to various adverse consequences such as falls, cognitive decline, infections, hospitalization, disability, institutionalization, and death. Pre-frailty is a condition predisposing and usually preceding the frailty state. Early detection of frailty (i.e., pre-frailty) may present an opportunity to introduce effective management to improve outcomes. Exercise training appears to be the basis of such management in addition to periodic monitoring of food intake and body weight. However, various nutritional supplements and other probable interventions, such as treatment with vitamin D or androgen, require further investigation. Notably, many societies are not conscious of frailty as a health problem. In fact, people generally do not realize that they can change this unfavorable trajectory to senility. As populations age, it is reasonable to begin treating frailty similarly to other population-affecting disorders (e.g., obesity, diabetes or cardiovascular diseases) and implement appropriate preventative measures. Social campaigns should inform societies about age-related frailty and pre-frailty and suggest appropriate lifestyles to avoid or delay these conditions. In this article, we review current information concerning therapeutic interventions in frailty and pre-frailty and discuss whether a greater public awareness of such conditions and some preventative and therapeutic measures may decrease their prevalence.

Heart rate contribution to the clinical value of heart rate variability

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