Assessing stress variations in children during the strange situation procedure: comparison of three widely used respiratory sinus arrhythmia estimation methods

Abstract

Objective. The respiratory sinus arrhythmia (RSA) is a well-known marker of vagal activity that can be exploited to measure stress changes. RSA is usually estimated from heart rate variability (HRV). This study aims to compare the RSA obtained with three widely adopted methods showing their strengths and potential pitfalls. Approach. The three methods are tested on 69 healthy preschoolers undergoing a stressful protocol, the strange situation procedure (SSP). We compare the RSA estimated by the Porges method, the univariate autoregressive (AR) spectral analysis of the HRV signal, and the bivariate AR spectral analysis of HRV and respirogram signals. We examine RSA differences detected across the SSP episodes and correlation between the estimates provided by each method. Main results. The Porges and the bivariate AR approaches both detected significant differences (i.e. stress variations) in the RSA measured across the SSP. However, the latter method showed higher sensitivity to stress changes induced by the procedure, with the mean RSA variation between baseline and first separation from the mother (the most stressful condition) being significantly different among methods: Porges, −17.5%; univariate AR, −18.3%; bivariate AR, −23.7%. Moreover, the performances of the Porges algorithm were found strictly dependent on the applied preprocessing. Significance. Our findings confirm the bivariate AR analysis of the HRV and respiratory signals as a robust stress assessment tool that does not require any population-specific preprocessing of the signals and warn about using RSA estimates that neglect breath information in more natural experiments, such as those involving children, in which respiratory frequency changes are extremely likely.

Information Retrieval from Photoplethysmographic Sensors: A Comprehensive Comparison of Practical Interpolation and Breath-Extraction Techniques at Different Sampling Rates

“…A more robust estimate of the RSA from HRV requires respiratory rate information. In fact, knowing the frequency content of respiration allows us to refine the frequency range for HF power calculation [47] and enables the quantification of cardiorespiratory interactions through bivariate models [17][18][19][20]. The respirogram, i.e., the breath signal sampled at the occurrence of each heartbeat, carries all the respiratory information needed for such analyses.…”

“…In addition, like ECG [15], PPG signal also carries information about breathing activity, which modulates specific waveform characteristics (amplitude, frequency, and baseline wander). For this reason, such signal can also be exploited to derive respiratory measures of clinical interest [16], as well as a breath signal that can be used, for instance, to improve respiratory sinus arrhythmia (RSA) estimates from HRV by means of bivariate approaches [17][18][19][20] or for sleep apnea detection [21].…”