Assessment of dispersion patterns for negative stress detection from electroencephalographic signals

[摘要] Negative stress, or distress, represents a serious problem in advanced societies given its adverse conse-quences for health. Many studies have focused on the detection of distress from physiological signals such as the electroencephalogram (EEG). To this respect, the combination of regularity-based quadratic sample entropy (QSampEn) and symbolic amplitude-aware permutation entropy (AAPE) has reported valuable outcomes in distress recognition. In the present work, the recently introduced symbolic metric called dispersion entropy (DispEn) is applied for the first time to the same problem. Statistically significant re-sults reported by the single metric have demonstrated its capability for calm and distress detection. Fur-thermore, relevant differences have been found between the combination of QSampEn with either AAPE or DispEn, finding that the assessment of ordinal and dispersion patterns leads to distinct and comple-mentary outcomes. Finally, the combination of the three entropy metrics has considerably overcome the results ever reported by other indices in similar studies. (c) 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )

[发布日期] 2021-11-01

[关键词] Electroencephalography;Distress;Dispersion patterns;Nonlinear analysis