Demographic Characterization of Heart Rate Variability (HRV)

Demographic Characterization of Heart Rate Variability (HRV)

Ankur Ganguly (Siliguri Institute of Technology, India), D. N. Tibarewala (School of BioScience and Engineering, Jadavpur University, India), S. Dasgupta (Calcutta Institute of Engineering and Management, India) and Subhojit Sarker (Siliguri Institute of Technology, India)
Copyright: © 2015 |Pages: 12
DOI: 10.4018/978-1-4666-5888-2.ch039
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Introduction

The rate and modulation of the heart beat i.e. heart rate variability (HRV) are controlled by the autonomic nervous system. Sympathetic activation decreases and parasympathetic action of the heart increases HRV. Measurement of heart rate variability (HRV) provides a non-invasive method to obtain reliable and reproducible information on the autonomic modulation of heart rate and has become an important tool for risk assessment. Other than relating to various physiological and clinical correlations, HRV has been found to correlate with, e.g., age, mental and physical stress, and attention, sees, geographical locations, altitude, racial differences, etc. The Chapter is focused on demographic characterization of the Heart Rate Variability parameters. A short term (5 minutes) recording is performed on the subjects selected randomly in the plains (reference data) and hills (research data). HRV analysis is performed in three domains and statistically analyzed for characterization.

The objective being, to characterize the population of the north eastern hilly regions of West Bengal and to have a comparison with the population residing in the plains of the region using different analysis methods. Thus both male and female subjects from different parts of hills and plains of the region were selected for the present study. Five hundred (500) subjects were selected on stratified random selection basis from 2007 to 2010. The population is divided into two groups, viz.

  • 1.

    The hill population (400 healthy subjects): The sample of four hundred subjects representing the hill population has 262 males and 138 females aged from 17 to 76 with a mean age of 34± 13.07 and 17 to 86 years with a mean age of 34.1 ± 11.4 respectively.

  • 2.

    The plains population (100 healthy subjects): The sample of one hundred subjects representing the plains population has 66 males and 34 females aged from 17 to 56 with a mean age of 27.68± 9.394 and 18 to 81 years with a mean age of 28.1 ± 12.2006 respectively.

The data collection was done in two steps, viz. Schedule by asking questions and noting down the same in one part and physiological data (Heart Rate parameters) collected from the subjects under various conditions by using Suunto T6 Heart Rate Monitor. Analysis of the parameters was carried out using KUBIOS HRV 2.0 software. Entire statistical analyses were performed using SPSS 13.0. An ANN has been designed using MATLAB R2009b neural network toolbox to characterize a person whether he belongs to hill or plains. The network has been trained on a test population and has been validated with another set of data in both categories of population.

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Background

An extent of experimental works emphasize that various cardiovascular variables like heart rate and blood pressure fluctuate from beat-to-beat. Ancient Physicians noted the temporal fluctuations in cardiovascular signals but overlooked the possible significance of the beat-to-beat fluctuation. The variability was treated as noise and it was either ignored or averaged out. Stephen Hales (1733) became the pioneer to report beat-to-beat heart rate variability to be synchronous with respiration and obstetrics became the maiden field in which the potential clinical significance of beat-to-beat variability in cardiovascular signals was recognized. The importance of sinus arrhythmia was described in relation to fetal monitoring in 1965 (Heart rate variability, 1996; Bernston et al., 1997). The variability correlated with fetal viability as step down of beat-to-beat variability indicated fetal compromise (Heart rate variability, 1996). Earlier studies indicate HRV measurements to be based on simple measurements of RR intervals in diabetic studies.

An in depth research have shown that decreased fluctuation of RR interval is not noise, but implicates an increased risk for arrhythmic events and an increased mortality rate in patients with a previous myocardial infarction. Time - frequency domain measures and nonlinear studies of heart rate variability have provided portent information and increased possibilities to perform noninvasive studies on the significance of changes in the regulation of heart rate behavior.

Key Terms in this Chapter

Artificial Neural Network (ANN): A computing system made up of a number of simple, highly interconnected processing elements, which mimics the biological neural network in order to process information by their dynamic state response to external inputs.

Correlation Dimension (CD): Can be considered as a measure for the number of independent variables needed to define the total system, here the cardiovascular system generating the RR interval time series, in phase space.

Electrocardiography: A trans-thoracic interpretation of the electrical activity of the heart over a period of time, as detected by electrodes attached to the surface of the skin and recorded by a device external to the body. The recording produced by this non-invasive procedure is termed an electrocardiogram (also ECG or EKG).

Heart Rate Variability (HRV): The variations between consecutive heartbeats. The rhythm of the heart is controlled by the sino-atrial (SA) node, which is modulated by both the sympathetic and parasympathetic branches of the autonomic nervous system.

QRS Complex: A name for the combination of three of the graphical deflections seen on a typical electrocardiogram (ECG).

Power Spectral Density (PSD): The periodic oscillations of the heart rate signal decomposed at different frequencies and amplitudes; and provides information on the amount of their relative intensity (termed variance or power) in the heart's sinus rhythm. The power spectrum consists of frequency bands ranging from 0 to 0.5 Hz and can be classified into four bands: the ultra-low frequency band (ULF), the very low frequency band (VLF), the low frequency band (LF) and the high frequency band (HF).

Approximate Entropy (ApEn): A family of regularity measures that quantifies how predictable the fluctuations in a time series are.

Autonomic Nervous System: ( ANS or Visceral Nervous System or Involuntary Nervous System): The part of the peripheral nervous system that acts as a control system, functioning largely below the level of consciousness, and controls visceral functions.

Time Domain Measures of HRV: SDNN- Standard deviation of all normal to normal R-R (NN) intervals, SDANN- Standard deviation of 5-minute average NN intervals, ASDNN- (index) Mean of the standard deviations of all NN intervals for all 5-minute segments in 24 hours, RMSSD- Square root of the mean of the squares of successive NN interval differences, NN50- The number of NN intervals differing by > 50 ms from the preceding interval, pNN50 - The percentage of intervals > 50 ms different from preceding interval.

Detrended Fluctuation Analysis (DFA): Measures the correlation within the signal. In DFA the correlations are divided into short-term and long-term fluctuations.

Sample Entropy: The negative natural logarithm of an estimate of the conditional probability that subseries (epochs) of length m that match point-wise within a tolerance r also match at the next point.

Heart Rate: The number of heartbeats per unit of time, typically expressed as beats per minute (bpm).

Poincare Plot: A graphical representation of the correlation between successive RR intervals, i.e. plot of RRj+1 as a function of RRj.

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