Heart Rate Variability: Measures and Models

International Journal of Engineering Research and Technology (IJERT), 2013

Review of Heart Rate Variability Analysis and its Measurement https://www.ijert.org/research/review-of-heart-rate-variability-analysis-and-its-measurement-IJERTV2IS2466.pdf Electrocardiogram (ECG) illustrates the electrical activity in the heart, and is the most important physiological parameter that gives the correct assessment regarding the functioning of the heart. The QRS complex is a prominent waveform in an ECG that gives the basis for analyzing heart rate variability (HRV). HRV is referred as the beat-to-beat alterations in heart rate. Commercial devices these days provide preset computerized measurement of HRV, thus providing the cardiologist a simple tool for both research and clinical learning. For obtaining meaningful data from the ECG, a noise free inter-beat interval (IBI) time series is required to be extracted. This is realized by the use of standard peak detection algorithms. The aim of this paper is to describe the various QRS detection techniques to analyze HRV. In this paper reviews for various time and frequency domain HRV parameters are also included. The significance and meaning of these different measures of HRV are a potential area of research and clinical approaches toward pathological detection.

Frontiers in physiology, 2012

This paper reviews the methods used for editing of the R-R interval time series and how this editing can influence the results of heart rate (HR) variability analyses. Measurement of HR variability from short and long-term electrocardiographic (ECG) recordings is a non-invasive method for evaluating cardiac autonomic regulation. HR variability provides information about the sympathetic-parasympathetic autonomic balance. One important clinical application is the measurement of HR variability in patients suffering from acute myocardial infarction. However, HR variability signals extracted from R-R interval time series from ambulatory ECG recordings often contain different amounts of artifact. These false beats can be either of physiological or technical origin. For instance, technical artifact may result from poorly fastened electrodes or be due to motion of the subject. Ectopic beats and atrial fibrillation are examples of physiological artifact. Since ectopic and other false beats a...

Annals of Noninvasive Electrocardiology, 1999

ABSTRACT Background: Interest in the analysis of RR interval variability by new dynamic methods is growing, because these methods give different, complementary information on heart rate behavior compared to the traditional measures of HRV. Several new methods have been recently introduced for studying complex heart rate dynamics, but the validity of these methods in analyzing RR interval behavior from Holter recordings is not well established.Methods: A real-time microprocessor QRS detector system using a 1-ms timing accuracy and a conventional 24-hour ECG recording system with a sampling frequency of 128 Hz were compared in the measurement of new dynamic components of HRV in a sample of ten healthy subjects and ten patients with acute Ml.Results: Significant errors were observed in the analysis of approximate entropy (ApEn; mean difference 11 %) and in the short-term scaling exponent value a1 (mean difference 3%) in detrended fluctuation analysis (DFA). In the Poincaré analysis, notable differences were also observed in short-term instantaneous RR-interval variability (SD1; mean difference 5%). In spectral analysis, low-to-high frequency ratio was also influenced by the sampling frequency (mean difference 14%). The magnitude of all these differences grew proportionately when the overall heart HRV diminished.Conclusions: The data suggest that conventional Holter recordings should be used with caution in the analysis of new dynamic HRV parameters, especially approximate entropy and the ratio between low and high frequency spectral components, particularly in patient populations with low total HRV.

Frontiers in physiology, 2015

2024

In this document I introduce to the analysis of five-minute recordings of R-R intervals, the intervals between two R waves of the heart. I show how to calculate in different ways the variance of this time series, which goes under the name of Heart Rate Variability (HRV); I also show how to apply the discrete Fourier transform to the signal and how to study the resulting harmonics. The biological meanings of these measures are also introduced. To exemplify the theory, I perform a study on about 500 daily recordings of a single subject using the R package RHRV. I also present the results of a small study on ME/CFS patients and matched healthy controls.

APCBEE Procedia, 2013

The heart is a key component of the human body, acting as a pump that transfers oxygenated and deoxygenated blood around the body. Like all other organs, it is susceptible to diseases and age. Heart rate variability (HRV) is the physiological phenomenon of variation in the time interval between heartbeats. It is measured by the variation in the beatto-beat interval. Its variation may contain indicators of current disease, or warnings about impending cardiac diseases. The indicators may be present at all times or may occur at random-during certain intervals of the day. It is strenuous and time consuming to study and pinpoint abnormalities in voluminous data collected over several hours. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics. In this paper we have discussed the various applications of HRV and different linear, frequency domain, wavelet domain, nonlinear techniques used for the analysis of the HRV.

Clinical Cardiology, 1990

Ann Noninvasive Electrocardio, 1996

2009

Among the techniques used in its evaluation, the heart rate variability (HRV) has arising as a simple and non-invasive measure of the autonomic impulses, representing one of the most promising quantitative markers of the autonomic balance. The HRV describes the oscillations in the interval between consecutive heart beats (RR interval), as well as the oscillations between consecutive instantaneous heart rates. It is a measure that can be used to assess the ANS modulation under physiological conditions, such as wakefulness and sleep conditions, different body positions, physical training and also pathological conditions. Changes in the HRV patterns provide a sensible and advanced indicator of health involvements. Higher HRV is a signal of good adaptation and characterizes a health person with efficient autonomic mechanisms, while lower HRV is frequently an indicator of abnormal and insufficient adaptation of the ANS, provoking poor patient's physiological function. Because of its importance as a marker that reflects the autonomic nervous system activity on the sinus node and as a clinical instrument to assess and identify health involvements, this study reviews conceptual aspects of the HRV, measurement devices, filtering methods, indexes used in the HRV analyses, limitations in the use and clinical applications of the HRV.

2012

The aim of the present contribution is to give a review on a new methodology that we may use when we are employed in the analyis of one of the most fundamental signals that we encounter in electrophysiology, the R-R intervals in analysis of the ECG. First of all the limits of the current FFT application are discussed. Soon after the basic foundations of the CZF method are exposed and we expose and discuss in detail a large number of physiological and clinical applications, based directly on experimental results .The results evidence the importance to use the CZF method as non invasive marker in analysis of HRV.