A fast permutation entropy for pulse rate variability online analysis with one-sample recursion

Heart rate dynamics for cognitive load estimation in a driving simulation task

Cognitive load (CL) is one of the leading factors moderating states and performance among drivers. Heavily increased CL may contribute to the development of mental stress. Averaged heart rate (HR) and heart rate variability (HRV) indices are shown to reflect CL levels in different tasks. The aim of this large-scale study was to explore how accurately HR and HRV metrics can differentiate between varying CL conditions during driving. Participants (N = 892, 44% female, from 18 to 79 years old) performed simulated driving in highway and urban scenarios. The n-back task was used as a mental distraction to further increase CL. The results have shown that increased CL was accompanied by higher HR, lower HRV, as measured by RMSSD, and higher HR complexity, as measured by permutation entropy. HR displayed the highest accuracy in discriminating between short windows (30 s) of different CL conditions, particularly highway versus urban driving and mental distraction during highway driving. We found gender and age effects on discriminative accuracy of HR and HRV metrics which were related to subjective ratings of CL. These results illustrate that HR and HRV indices provide a valid source for applications in the field of CL monitoring and mental stress detection.

Slope Entropy Characterisation: An Asymmetric Approach to Threshold Parameters Role Analysis

Slope Entropy (SlpEn) is a novel method recently proposed in the field of time series entropy estimation. In addition to the well-known embedded dimension parameter, m, used in other methods, it applies two additional thresholds, denoted as δ and γ, to derive a symbolic representation of a data subsequence. The original paper introducing SlpEn provided some guidelines for recommended specific values of these two parameters, which have been successfully followed in subsequent studies. However, a deeper understanding of the role of these thresholds is necessary to explore the potential for further SlpEn optimisations. Some works have already addressed the role of δ, but in this paper, we extend this investigation to include the role of γ and explore the impact of using an asymmetric scheme to select threshold values. We conduct a comparative analysis between the standard SlpEn method as initially proposed and an optimised version obtained through a grid search to maximise signal classification performance based on SlpEn. The results confirm that the optimised version achieves higher time series classification accuracy, albeit at the cost of significantly increased computational complexity.