An Explainable Fuzzy Theoretic Nonparametric Deep Model for Stress Assessment Using Heartbeat Intervals Analysis
This study presents an explainable fuzzy theoretic nonparametric deep model for an analysis of heart rate variability in application to stress assessment. We are concerned with the development of a model that evaluates and explains a short-time (3-5 minutes long) heartbeat interval sequence of an individual to estimate the level of acute perceived stress on a numerical scale from 0 to 100 via monitoring the functioning of the autonomic nervous system. The salient features of the approach are: a) A deep model, consisting of a nested composition of mappings, discovers layers of increasingly abstract heartbeat interval data representation. b) An analytical solution of the deep model's learning problem facilitates inducing a mapping from the non-interpretable heartbeat-interval-data-space onto another interpretable domain spanned by a stress index. A given non-interpretable R-R interval feature vector is explained by a) estimating the corresponding stress value, b) providing the weights which must be assigned to the subjective ratings of stress, and c) providing various information about the sympathetic and parasympathetic activities of autonomic nervous system by analyzing R-R interval sequence in frequency domain at different abstraction levels. The proof-of-concept is provided by experimentation on a previously studied dataset of 50 subjects and a new dataset of 100 subjects.