An Explainable Fuzzy Theoretic Nonparametric Deep Model for Stress Assessment Using Heartbeat Intervals Analysis

This article 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 min 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 the following. 1) A deep model, consisting of a nested composition of mappings, discovers layers of increasingly abstract heartbeat interval data representation. 2) An analytical solution of the deep model's learning problem facilitates inducing a mapping from the noninterpretable heartbeat-interval-data-space onto another interpretable domain spanned by a stress index. A given noninterpretable R- R interval feature vector is explained by: 1) estimating the corresponding stress value; 2) providing the weights which must be assigned to the subjective ratings of stress; and 3) 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.

Automated summary

This article introduces an explainable fuzzy theoretic nonparametric deep model for analyzing heart rate variability to assess stress levels based on short-term heartbeat interval sequences. The model utilizes a nested composition of mappings to capture abstract representations of the data and provides an analytical solution to map the heartbeat interval data onto a interpretable stress index domain. Through experimentation on datasets of 50 and 100 subjects, the model successfully estimates stress values and provides insights into autonomic nervous system activities.