Breathing Rate Complexity Features for “In-the-Wild” Stress and Anxiety Measurement

Breathing Rate Complexity Features for “In-the-Wild” Stress and Anxiety Measurement. Tiwari, A., Narayanan, S., & Falk, T. H. In 2019 27th European Signal Processing Conference (EUSIPCO), pages 1-5, Sep., 2019.
doi abstract bibtex

Features extracted from respiratory activity signals have been shown to carry information about mental states such as anxiety and mental stress. Such findings, however, are based on studies conducted mostly in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate natural settings (i.e., “in-the-wild”) remains unknown. In this paper, we propose new non-linear complexity measures computed from four different respiration activity time series (i.e., inter-breath interval, inhale-to-exhale ratio, inhale/exhale amplitude envelope, and interbreath difference) and show their discriminatory power for anxiety and stress monitoring in the workplace. The new features are tested on a dataset collected from 200 hospital workers (nurses and staff) during their normal work shifts. The proposed features are shown to be complementary to conventional measures of breathing rate and depth.

@InProceedings{8902700,
  author = {A. Tiwari and S. Narayanan and T. H. Falk},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Breathing Rate Complexity Features for “In-the-Wild” Stress and Anxiety Measurement},
  year = {2019},
  pages = {1-5},
  abstract = {Features extracted from respiratory activity signals have been shown to carry information about mental states such as anxiety and mental stress. Such findings, however, are based on studies conducted mostly in controlled laboratory environments with artificially-induced psychological responses. While this assures that high quality data are collected, the amount of data is limited and the transferability of the findings to more ecologically-appropriate natural settings (i.e., “in-the-wild”) remains unknown. In this paper, we propose new non-linear complexity measures computed from four different respiration activity time series (i.e., inter-breath interval, inhale-to-exhale ratio, inhale/exhale amplitude envelope, and interbreath difference) and show their discriminatory power for anxiety and stress monitoring in the workplace. The new features are tested on a dataset collected from 200 hospital workers (nurses and staff) during their normal work shifts. The proposed features are shown to be complementary to conventional measures of breathing rate and depth.},
  keywords = {diseases;feature extraction;medical signal processing;patient care;pneumodynamics;psychology;time series;artificially-induced psychological responses;ecologically-appropriate natural settings;nonlinear complexity measures;inter-breath interval;stress monitoring;respiratory activity signals;mental states;breathing rate complexity features;anxiety measurement;respiration activity time series;in-the-wild stress;feature extraction;Feature extraction;Stress;Benchmark testing;Biomedical measurement;Stress measurement;Time series analysis;Entropy},
  doi = {10.23919/EUSIPCO.2019.8902700},
  issn = {2076-1465},
  month = {Sep.},
}

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