Feasibility of Linear Parametric Estimation of Dynamic Information Measures to assess Physiological Stress from Short-Term Cardiovascular Variability

Abstract

Extensive efforts have been recently devoted to implement fast and reliable algorithms capable of assessing the physiological response of the organism to physiological stress. In this study, we propose the comparison between model-free and linear parametric methods as regards their ability to detect alterations in the dynamics and in the complexity of cardiovascular and respiratory variability evoked by postural and mental stress. Dynamic entropy (DE) and information storage (IS) measures were calculated on three physiological time-series, i.e. heart period, respiratory volume and systolic arterial pressure, on 61 healthy subjects monitored in resting conditions as well as during head-up tilt and while performing a mental arithmetic task. The results of the comparison suggest the feasibility of DE and IS measures computed from different physiological signals to discriminate among resting and stress states. If compared to the model-free algorithm, the faster linear method appears to be capable of detecting the same (or even more) statistically significant variations of DE or IS between resting and stress conditions, being thus in perspective more suitable for the integration within wearable devices. The computation of entropy indices extracted from multiple physiological signals acquired through wearables will allow a real-time stress assessment on people during daily-life situations.

Feasibility of Ultra-Short Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and During Stress via Time-domain and Entropy-based Measures

“…Physiologically, this can be ascribed to the augmented involvement of upper brain centres in controlling the vascular dynamics and resistance associated with sympathetic activation. Relatively complex pattern of vascular resistance changes (an increase in splanchnic and skin regions associated with its decrease in limbs) results in an augmented SAP dynamical complexity, as demonstrated by previous works [20–22,63,66]. The trend towards lower SAP complexity values during tilt may be related to the synchronization of peripheral vascular activity due to sympathetic activation, contributing to regularize the fluctuations of SAP [67].…”

“…This may be due again to the significant proportion of nonlinear dynamics contributing also to UST HRV and cardiovascular variability [20,24,69] detected by knn estimator but neglected by model-based parametric approach. Also in this case, the increased discriminative capability of linear estimator and its lower computational costs may be exploited for discrimination between rest and stress conditions [22]. Furthermore, our results demonstrate that using shorter time series also requires reduced computational costs for both estimators, with a decrease of ∼1.7 times for lin and ∼3.1 times for knn when shortening the time series length from 300 to 120 samples.…”

“…This finding is difficult to explain, given that the strong nonlinear dynamics contributing to short-term HRV and cardiovascular variability [20,24,69,70] are detected by knn estimator but neglected by model-based parametric approach. The augmented discriminative capability of linear estimator, even if due to the non-linear dynamics present in the phenomenon but not properly taken into account, may be even in perspective used in practical application for a more accurate and fast classification between rest and stress conditions [22,41]. This is also reinforced by the very low computational times required for the lin estimator to compute the entropy-based measures on 300-sample series length (ST standard), which is 24 times lower if compared to knn .…”

“…Short-term HRV is commonly investigated through different time-, frequency-and information-theoretic domain indexes computed starting from ECG R-R interval time series. Specifically, time-domain indexes are used to quantify both average heart rhythm and the extent of beat-to-beat variability [15,17], while frequency-domain indexes extract information specific to various time scales of oscillations; furthermore, more recently developed entropy-based measures permit to assess the regularity and complexity of cardiovascular dynamics [18][19][20][21][22][23][24]. ST HRV analysis has been proven useful also outside clinical settings, e.g.…”

Low-invasive multisensor real-time acquisition system for the assessment of cardiorespiratory and skin conductance parameters

“…The implementation of other acquisition protocols aimed at causing orthostatic and mental stress could be useful to assess the responses of the organism and further test our system. Future works could regard the implementation, within the firmware of the portable system, of linear algorithms for estimation of dynamic information measures to assess physiological activity and the stress level [35], [36]. Finally, a future upgrade of the system foresees the realization a novel prototype completely wearable, to be easily and comfortably employed for a continuous daily monitoring of health-status and quality of life.…”