Unconstrained Estimation of HRV Indices After Removing Respiratory Influences From Heart Rate

Abstract

This paper proposes an approach to better estimate the sympathovagal balance (SB) and the respiratory sinus arrhythmia (RSA) after separating respiratory influences from the heart rate (HR). Methods: The separation is performed using orthogonal subspace projections and the approach is first tested using simulated HR and respiratory signals with different spectral properties. Then, RSA and SB are estimated during autonomic blockade and stress using the proposed approach and the classical heart rate variability (HRV) analysis. Both real and ECG-derived respiration (EDR) are used and the reliability of the EDR is evaluated. Results: Mean absolute percentage errors lower than 1% were obtained after removing previously known respiratory signals from simulated HR. The proposed indices were able to improve the quantification of SB during autonomic withdrawal. In the stress data, differences (p < 0.003) among relaxed and stressful phases were found with the proposed approach, using both the real respiration and the EDR, but they disappeared when using the classical HRV. Conclusion: A better assessment of the autonomic nervous system' response to pharmacological blockade and stress can be achieved after removing respiratory influences from HR, and this can be done using either the real respiration or the EDR. Significance: This work can be used to better identify vagal withdrawal and increased sympathetic activation when the classical HRV analysis fails due to the respiratory influences on HR. Furthermore, it can be computed using only the ECG, which is an advantage when developing wearable systems with limited number of sensors.

Heart Rate Variability and Exceptional Longevity

“…The interpretation of HF power has been also challenged, especially when the respiratory rate does not fall within the HF band (0.15-0.4 Hz; Laborde et al, 2017). Different approaches have been proposed to overcome this limitation by redefining the HF band (Bailón et al, 2007;Varon et al, 2018). It has also been suggested that sympathetic neural activity may modulate the HF component (Billman, 2013).…”

Pulse Oximetry Markers for Cardiovascular Disease in Sleep Apnea

“…In a 120 second time window around each oxygen desaturation, the median value, standard deviation and difference between minimum and maximum value (delta) were computed for each of these time series. Additionally, the degree of cardio-respiratory interactions was estimated by the power in the respiratory component of the PRV, obtained using orthogonal subspace projections [10], taking the high pass filtered PAV and PWV as surrogates of the respiratory signal. The obtained PPG parameters were then averaged per patient, as done for the SpO 2 features.…”

Influence of the Heart Rate Variability Representations on the Quantification of the Cardiorespiratory Interactions

“…As discussed in [7] and as observed in Figure 2, the quantification of the RSA using the HFn parameter tends to underestimate the respiratory modulation on the HRV because the breathing rates tend to fall bellow 0.15 Hz in this dataset. Therefore, the respiratory information is mainly contained in the LF band and this is not captured by the HFn parameter.…”

“…• Orthogonal Subspace Projections (OSP) [7]: This method was used to decompose each HRV representation into a respiratory component (HRV resp ) and a residual component. The relative power of HRV resp (P resp ) was used to quantify the dynamics of the HRV linearly related to the respiration and it was calculated as:…”

“…For this reason, alternative methods have been proposed for the quantification of the RSA. Two of them are the subspace projections [7] and a Time-Frequency (TF) representation [8], which are evaluated in this study. The aim of this paper is to evaluate the influence of the HRV representations on the aforementioned RSA quantifications.…”