Time-varying assessment of heart rate variability parameters using respiratory information
Introduction
Heart rate variability (HRV) analysis has been widely used to assess the function of autonomic nervous system (ANS) in regulation of cardiovascular system [1], [2]. ANS activity has been investigated through analysis of HRV —also known as R-R interval (RRI) variability— or studying the interaction between heart rate (HR), blood pressure (BP) and respiration variations [3], [4]. HR is affected by BP through the baroreflex system, while respiration mediates cardiovascular parameters through both ANS and mechanical paths [5].
In general, two frequency components in HR spectrum are of significance: low frequency (LF) component (with a typical band of 0.04–0.15 Hz) which is thought to be affected by both sympathetic and parasympathetic tones, and high frequency (HF) component (0.15–0.4 Hz) which is mostly mediated by respiration and known as respiratory sinus arrhythmia [1], [6]. HF component is mainly of parasympathetic (vagal) origin. The ratio between the powers of these two components (LF/HF) is considered as a measure of sympatho-vagal balance [1].
Some indices of HRV in stable conditions (e.g., time-domain indices including statistical and geometrical indices and frequency-domain indices like LF/HF ratio) have been established for clinical diagnosis of ANS function [7]. Furthermore, time-varying analysis of HRV is of importance in order to assess HRV parameters continuously in healthcare monitoring systems or during non-stationary conditions. Different parametric and non-parametric methods have been considered for this purpose [1], [4], [8], [9], [10], [11], [12], [13]. The main shortcoming of most HRV studies is that they do not consider respiration information. When the respiration rate is very low, the HF component of HRV interferes with the typical LF band. This leads to an unreliable estimation of the LF and/or HF powers [14], [15], [16]. To address this problem, some studies have considered adaptable frequency bands depending on the respiration frequency [17]. Commonly, a band with the center frequency equal to the respiration rate is considered for the HF component. In these approaches, the respiration frequency is measured from a respiratory-related signal [18], [19], [20], [21], [22], [23].
While some studies benefit from respiration information in HRV analysis for better definition of the HF band, some others have tried to remove respiratory-related oscillations from HR. It is thought that using the non-respiratory related part of HR signal in HRV or HR-BP coupling analysis results in derivation of more relevant ANS or baroreflex indices [6]. To this end, different methods have been used, e.g., adaptive filtering, orthogonal subspace projection and multiscale principal component analysis (PCA) [6], [24] and autoregressive moving average with exogenous input (ARMAX) analysis with the respiration-related signal as the input of the model [25], [26]. The performance of these different methods have been investigated in [6] and it has been concluded that ARMAX and orthogonal subspace projection work better than other approaches in separating RRI components.
Our study specifically addresses the instantaneous separation of RRI components and time-varying estimation of HRV parameters simultaneously using respiration information. An ARMAX model for the RRI series with the respiratory signal as the exogenous input is considered for this sake. As mentioned before, the efficiency of ARMAX in separation of respiratory-related component of HR has been reported previously [6]. In this study, we specifically employ the ARMAX model in the context of time-varying estimation of HRV parameters by parametrically modeling the respiratory signal as a sinusoid. This joint modeling enables us to estimate time-varying HRV parameters without the need to consider pre-defined LF and HF bands. Smoothed extended Kalman filtering (SEKF) is used for time-varying estimation of the model parameters. Kalman filter is a powerful tool for state estimation even when the precise nature of the model is unknown. SEKF avoids the lag error presented by the normal Kalman filtering [9].
HRV analysis using respiratory information normally needs an additional device to be connected to the body like strain-gauge belts, nasal thermistors or capnogram to record the respiration, which is uncomfortable and may be contraindicated for certain patients. Therefore, we also investigate the possibility of using pulse transit time (PTT) and the amplitude of photoplethysmogram () as the input of our model. These signals, especially , have been used in other related contexts, e.g., estimation of respiration rate [18], [27], [28]. Herein, we study their efficiency as a reference for respiratory-related oscillations in our HRV analysis framework.
The paper is organized as follows. Section II describes the synthetic and real data and the proposed model. In addition, time-varying estimation of the model parameters using SEKF is explained. Section III presents the results on the separation and power estimation for synthetic and real data. Section 4 Discussion, 5 Conclusion are devoted to discussion and conclusion respectively.
Section snippets
Synthetic data
Different synthetic signals are generated using a physiologically based model. The model includes parts for the baroreflex and cardiovascular couplings and has been described and validated elsewhere [29], but we briefly describe it here for the sake of completeness. The cardiovascular system is represented by a 2-element Windkessel model. Physiologically based equations explain the baroreflex effects on HR through separate sympathetic and parasympathetic ways. The ANS-stimulating signal
Step change in the frequencies of LF and HF components (scenario 1)
Fig. 2 shows the respiration frequency estimated by the Kalman smoother algorithm () together with the actual respiration rate ( in eqn. (1)) as the reference. Fig. 3a shows the constructed respiration signal by (6) using the estimated parameters. The LF and HF time series of RRI estimated by the first and second parts of (3), respectively, are shown in Fig. 3b together with the real synthetic RRI signal. Figs. 3c and d show the frequency spectra of the estimated LF and HF components of
Discussion
As Fig. 4, Fig. 7 show, our proposed joint model performs better than AR model in estimating the powers of LF and HF components of HR for the different synthetic signals. In Fig. 4, when the frequencies change at the time point 250 s and HF spectrum gets into the typical LF band, the AR model underestimates and overestimates the HF and LF powers, respectively. For the chirp varying frequency signal, AR is unable to estimate the powers of LF and HF components properly before the approximate time
Conclusion
This study has presented a method for time-varying separation of HRV components and estimation of HRV classical parameters simultaneously based on a modified ARMAX model for the RRI series jointly used with a parametric model of respiratory-related signal. AR modeling is commonly used in the context of HRV parameter estimation which gives good frequency and reasonable time resolutions. Compared to AR, our joint model is especially effective in the case of interference of respiration rate with
References (43)
- et al.
Kubios HRV–heart rate variability analysis software
Comput. Methods Programs Biomed.
(2014) - et al.
Improved characterization of HRV signals based on instantaneous frequency features estimated from quadratic time–frequency distributions with data-adapted kernels
Biomed. Signal Process. Control
(2014) - et al.
Analysis of heart rate variability during exercise stress testing using respiratory information
Biomed. Signal Process. Control
(2010) - et al.
A robust approach for ECG-based analysis of cardiopulmonary coupling
Med. Eng. Phys.
(2016) - et al.
Separating the effect of respiration on the heart rate variability using Granger's causality and linear filtering
Biomed. Signal Process. Control
(2017) Heart rate variability standards of measurement, physiological interpretation, and clinical use
Eur. Heart J.
(1996)- et al.
A unified point process probabilistic framework to assess heartbeat dynamics and autonomic cardiovascular control
Eng. Approaches Study Cardiovasc. Physiol. Model. Estim. Signal Process.
(2012) - et al.
Characterization of dynamic interactions between cardiovascular signals by time-frequency coherence
IEEE Trans. Biomed. Eng.
(2012) - et al.
Respiratory sinus arrhythmia: autonomic origins, physiological mechanisms, and psychophysiological implications
Psychophysiology
(1993) - et al.
Separation of respiratory influences from the tachogram: a methodological evaluation
PloS One
(2014)
Heart rate variability: a review
Med. Biol. Eng. Comput.
On the quantification of heart rate variability spectral parameters using time–frequency and time-varying methods
Phil. Trans. Roy. Soc.Lond. Math. Phys. Eng. Sci.
Time-varying analysis of heart rate variability signals with a kalman smoother algorithm
Physiol. Meas.
Spectral analysis of heart rate variability with the autoregressive method: what model order to choose?
Comput. Biol. Med.
Wavelet analysis of instantaneous heart rate: a study of autonomic control during thrombolysis
Am. J. Physiol. Regul. Integr. Comp. Physiol.
Application of empirical mode decomposition to heart rate variability analysis
Med. Biol. Eng. Comput.
Is the normal heart rate chaotic due to respiration?
Chaos Interdiscip. J. Nonlinear Sci.
Standard heart rate variability spectral analysis: does it purely assess cardiac autonomic function?
Europace
The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance
Front. Physiol.
Data fusion for improved respiration rate estimation
EURASIP J. Adv. Signal Process.
Multiparameter respiratory rate estimation from the photoplethysmogram
IEEE Trans. Biomed. Eng.
Cited by (7)
Spectral Analysis of Heart Rate Variability in Time-Varying Conditions and in the Presence of Confounding Factors
2024, IEEE Reviews in Biomedical EngineeringCuffless Blood Pressure Estimation Using Cardiovascular Dynamics
2022, International Conference on Electrical, Computer, and Energy Technologies, ICECET 2022Derivation of Frequency Components from Overnight Heart Rate Variability Using an Adaptive Variational Mode Decomposition
2021, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBSUnconstrained Estimation of HRV Indices after Removing Respiratory Influences from Heart Rate
2019, IEEE Journal of Biomedical and Health Informatics