Application of an automatic adaptive filter for Heart Rate Variability analysis
Introduction
The successive values in the instantaneous heart rate referred to as RR intervals or NN intervals [1], which are used in the Heart Rate Variability (HRV) analysis [2], [3], [4], are displayed in a plot (tachogram). The horizontal axis of this tachogram shows the events over time while the vertical axis displays the values of the intervals between heartbeats. In general, the analysis of tachograms or experimental time series requires that these series be properly filtered in a way that excludes ectopic beats or noise effects.
For sequential analysis of intervals between heartbeats it is essential to remove artifacts, impulses not initiated by the sinoatrial node (ventricular premature complexes – VPC) [5] and spurious interference such as those due to muscle tremors, poor placement of electrodes, atypical electronics effects on the environment to capture the signal, and so forth.
Fig. 1 shows two tachograms each with 1750 RR intervals. Fig. 1A contains a lot of artifacts that must be excluded (RR intervals with extremely high or low values), while Fig. 1B shows a variability compatible with clinical behavior.
In analysing HRV it is necessary that the heart rhythm be sinusal (the origin is from the sinus node). Atrial or ventricular extrasystoles (whereby heartbeats are initiated by the atrial or ventricular cells rather than by the sinus node) are not the deciding factor when it comes to the issue of excluding artifacts, but instead several other situations that can render a tachogram unsuitable for analysis. In these situations the “human eye” is considered the gold standard [6] to discern whether to use a particular RR tachogram pattern or not.
As discussed in previous articles Storck et al. [7] and Karlsson et al. [8], the use of a computational implemented preprocessing procedure could be a very convenient tool to aid an expert in the manual analyses, allowing one to accelerate the procedure of analyzing a large amount of data.
This adaptive filter can self-adjust its parameters according to the input signal and improve its analytical performance [9]. The main objective of this study is to apply and compare the adaptive filter method for time series of HRV analysis with the conventional filter method by considering different clinical situations. Such clinical situations include: premature newborns hospitalized in intensive care units, full-term newborns, healthy young adults, adults submitted to a very-low-calorie diet for the purpose of weight loss, and adults in the preoperative evaluation for coronary artery bypass grafting for severe coronary disease.
The comparison between the filtered series using conventional and adaptive methods is performed by statistical analysis of HRV indexes calculated in the frequency domain using nonlinear techniques (Poincaré plot and entropy-based analysis).
Section snippets
Material and methods
This section describes the HRV data used, the conventional and adaptive filters, and also statistical analysis of HRV features using frequency-domain and nonlinear descriptors (Poincaré plot, approximate entropy and sample entropy).
Results
Free parameters of the filter were chosen on an empirical basis but in accordance with and following the analyses carried out by a specialist, as a training process to setting the filter final parameters. The values of the parameters used are: c = 0.05, ρ = 10, a = 3 e σb = 0.02.
Two representative examples of application of the adaptive fiter are given below for two time series. The first time series is an example with relatively few artifacts and the second one is an example with a large number of
Discussion
In our analysis, the filter parameters have been adjusted to be used for the whole set of data (see supplementary data). This approach makes the filter more robust so that it could be used for all groups of RR time series (without ectopic beats), in addition to standardizing the use of the adaptive filter for all time series presented in this study.
Through comparison with the conventional method, which requires the intervention of an expert to manually remove ectopic beats in the processing of
Funding
None.
Conflict of interest statement
None declared.
Ethical approval
Not required.
Acknowledgments
The authors thank CAPES/Brazil and CNPq/Brazil for financial support.
References (43)
Status of cardiovascular issues related to space flight: implications for future research directions
Respiratory Physiology & Neurobiology
(2009)- et al.
Computer analysis of antepartum fetal heart rate: 1. Baseline determination
International Journal of Bio-Medical Computing
(1990) - et al.
Linear and nonlinear analysis of heart rate patterns associated with fetal behavioral states in the antepartum period
Early Human Development
(2007) - et al.
Combining classical HRV indices with wavelet entropy measures improves to performance in diagnosing congestive heart failure
Computers in Biology and Medicine
(2007) - et al.
Analysis of lagged Poincaré plots in heart rate signals during meditation
Digital Signal Processing
(2011) Heart rate variability: standards of measurement, physiological interpretation, and clinical use
Circulation
(1996)Heart rate variability and autonomic diabetic neuropathy
Diabetes Nutrition & Metabolism
(2000)- et al.
Heart rate variability: a review
Medical & Biological Engineering & Computing
(2006) - et al.
Nonlinear analysis of complex phenomena in cardiological data
Herzschrittmachertherapie und Elektrophysiologie
(2000) - et al.
Diagnostic test for the discrimination between interictal epileptic and non-epileptic pathological EEG events using auto-cross-correlation methods
American Journal of Electroneurodiagnostic Technology
(2003)