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## Über dieses Buch

This book introduces readers to the basic concepts of Heart Rate Variability (HRV) and its most important analysis algorithms using a hands-on approach based on the open-source RHRV software. HRV refers to the variation over time of the intervals between consecutive heartbeats. Despite its apparent simplicity, HRV is one of the most important markers of the autonomic nervous system activity and it has been recognized as a useful predictor of several pathologies. The book discusses all the basic HRV topics, including the physiological contributions to HRV, clinical applications, HRV data acquisition, HRV data manipulation and HRV analysis using time-domain, frequency-domain, time-frequency, nonlinear and fractal techniques.

Detailed examples based on real data sets are provided throughout the book to illustrate the algorithms and discuss the physiological implications of the results. Offering a comprehensive guide to analyzing beat information with RHRV, the book is intended for masters and Ph.D. students in various disciplines such as biomedical engineering, human and veterinary medicine, biology, and pharmacy, as well as researchers conducting heart rate variability analyses on both human and animal data.

## Inhaltsverzeichnis

### Chapter 1. Introduction to Heart Rate Variability

Searching on Google Scholar the string “heart rate variability” (HRV) provides about half a million references, which gives us an idea of the research activity around this concept. This chapter describes the historical development of this research field, from the pioneering work of the eighteenth and nineteenth centuries to the boom of the final decade of the twentieth century. HRV analysis is important from a clinical point of view because of its direct relationship with the autonomic nervous system (ANS). Therefore, we include a section explaining the physiological basis of HRV and its relationship with the ANS. Finally, we review the most relevant clinical applications of HRV in three distinct lines: patient monitoring, acute care, and chronic disorders. The reader of this chapter must bear in mind that the purpose of this book is to present a software tool, RHRV, which greatly simplifies performing heart rate variability analysis. This chapter does not pretend to be a comprehensive review of the physiology of heart rate variation, but just a brief introduction to the field whose main purpose is building a common language to be able to present concepts more effectively throughout the book.
Constantino Antonio García Martínez, Abraham Otero Quintana, Xosé A. Vila, María José Lado Touriño, Leandro Rodríguez-Liñares, Jesús María Rodríguez Presedo, Arturo José Méndez Penín

The initial steps to work with RHRV functions are presented in this chapter. The process starts with the loading of records containing beat positions that should be preprocessed prior to frequency, time, or nonlinear analysis. Data can be stored in various types of files, and RHRV routines can deal with different data formats. Next, heart rate must be obtained from beat positions. It may occur that spurious points appear in the heart rate signal. RHRV allows users to delete these outliers, when necessary. Besides, the signal can be filtered to reject automatically points that do not correspond to acceptable physiological values.
Constantino Antonio García Martínez, Abraham Otero Quintana, Xosé A. Vila, María José Lado Touriño, Leandro Rodríguez-Liñares, Jesús María Rodríguez Presedo, Arturo José Méndez Penín

### Chapter 3. Time-Domain Analysis

Heart rate variability can be analyzed by several methods, being time-domain methods the simplest measures that can be obtained from RR intervals series. Numerical indices summarizing the variability of the series are calculated from the RR intervals series. These measures are easily implemented and have low computational cost. They are usually calculated over longer segments of data than frequency-domain methods, typically 24 h. However, they can be also estimated from smaller segments (typically 5 min) in order to compare different episodes. In this chapter, a review of time-domain measures and their calculation within RHRV will be shown.
Constantino Antonio García Martínez, Abraham Otero Quintana, Xosé A. Vila, María José Lado Touriño, Leandro Rodríguez-Liñares, Jesús María Rodríguez Presedo, Arturo José Méndez Penín

### Chapter 4. Frequency Domain Analysis

The sympathetic and parasympathetic branches of the ANS strongly influence heart rate. Afferent sympathetic activity increases heart rate, while afferent parasympathetic activity decreases heart rate. The speed with which changes in these systems are reflected in changes in heart rate is different. The sympathetic system is slow in its effects (a few seconds), while the parasympathetic system is faster (0.2–0.6 s). Given the different speeds of response, it is possible to use frequency analysis to study sympathetic and parasympathetic contributions to the HRV. A key fact to keep in mind in this analysis is that the RR series is not stationary. In this chapter, we will see how to perform HRV frequency analysis using RHRV.
Constantino Antonio García Martínez, Abraham Otero Quintana, Xosé A. Vila, María José Lado Touriño, Leandro Rodríguez-Liñares, Jesús María Rodríguez Presedo, Arturo José Méndez Penín

### Chapter 5. Nonlinear and Fractal Analysis

There are many complex systems in nature that can be explained by nonlinear interactions of its main components. The heart rate regulation is one of the most complex systems in the human body. Heart rhythm is innervated by both the parasympathetic and sympathetic branches of the ANS. At the same time, the autonomic nervous system is influenced by humoral effects, hemodynamic variables, respiratory rhythm, and stroke volume, among others. Furthermore, there exist feedback loops among these mechanisms influencing each other in a nonlinear way. A consequence of these nonlinear interactions is that heart rate modulation cannot be fully understood by studying its components in isolation. However, the study of the heart rhythm modulation as a whole is a formidable task. A more common approach in the literature, more modest than the full comprehension of the heart rate regulation system, is trying to quantify the complexity of heart rate using nonlinear statistics derived from the chaos theory or from fractal processes. In this chapter, we summarize the most widely used statistics based on nonlinear and fractal dynamics, and we show how to compute them with RHRV.
Constantino Antonio García Martínez, Abraham Otero Quintana, Xosé A. Vila, María José Lado Touriño, Leandro Rodríguez-Liñares, Jesús María Rodríguez Presedo, Arturo José Méndez Penín

### Chapter 6. Comparing HRV Variability Across Different Segments of a Recording

Intervals of physiological interest within the heartbeat time series (such as apnea and ischemia) may be marked making use of tags in so-called episodes. Episodic information can be incorporated into RHRV from external files or can be added using functions included in the package. This information can be useful to compare sections of HRV records both visually and numerically.
Constantino Antonio García Martínez, Abraham Otero Quintana, Xosé A. Vila, María José Lado Touriño, Leandro Rodríguez-Liñares, Jesús María Rodríguez Presedo, Arturo José Méndez Penín

### Chapter 7. Putting It All Together, a Practical Example

This chapter presents an example of a complete HRV analysis of several long-duration ECG records employing the RHRV software package, including frequency, nonlinear, and time analysis. First, 3-h intervals of each ECG were extracted, corresponding to morning, afternoon, and night periods. Next, HRV analyses in both time and frequency domains were performed over each portion, and nonlinear values were also estimated. Finally, statistical analysis was applied over the variability parameters corresponding to these three types of fragments, to verify if differences existed among the morning, afternoon, and night ECG intervals. Some statistically significant differences were found between the morning and night periods. In particular, HRVi (HRV index, time) and Poincaré $$SD_{2}$$ (nonlinear) parameters differ in a statistical way.
Constantino Antonio García Martínez, Abraham Otero Quintana, Xosé A. Vila, María José Lado Touriño, Leandro Rodríguez-Liñares, Jesús María Rodríguez Presedo, Arturo José Méndez Penín

### Backmatter

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