%0 Journal Article %1 9216097 %A Kumar, Mohit %A Zhang, Weiping %A Weippert, Matthias %A Freudenthaler, Bernhard %D 2021 %J IEEE Transactions on Fuzzy Systems %K deep-learning fuzzy image kumar mappings neural-networks partial %N 12 %P 3873-3886 %R 10.1109/TFUZZ.2020.3029284 %T An Explainable Fuzzy Theoretic Nonparametric Deep Model for Stress Assessment Using Heartbeat Intervals Analysis %U https://ieeexplore.ieee.org/document/9216097 %V 29 %X 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.

@article{9216097, abstract = {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.}, added-at = {2023-04-26T08:14:33.000+0200}, author = {Kumar, Mohit and Zhang, Weiping and Weippert, Matthias and Freudenthaler, Bernhard}, biburl = {https://www.bibsonomy.org/bibtex/28bb22fd8da710d07b04d7074c6146074/scch}, doi = {10.1109/TFUZZ.2020.3029284}, interhash = {526f2a0d40c36f84d570cfbcc135b1c9}, intrahash = {8bb22fd8da710d07b04d7074c6146074}, issn = {1941-0034}, journal = {IEEE Transactions on Fuzzy Systems}, keywords = {deep-learning fuzzy image kumar mappings neural-networks partial}, month = dec, number = 12, pages = {3873-3886}, timestamp = {2023-04-26T08:14:33.000+0200}, title = {An Explainable Fuzzy Theoretic Nonparametric Deep Model for Stress Assessment Using Heartbeat Intervals Analysis}, url = {https://ieeexplore.ieee.org/document/9216097}, volume = 29, year = 2021 }