Assessment of dispersion patterns for negative stress detection from electroencephalographic signals
Beatriz García-Martínez, Antonio Fernández‐Caballero, Raúl Alcaraz, Arturo Martínez‐Rodrigo
Abstract
Negative stress, or distress, represents a serious problem in advanced societies given its adverse consequences 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 results reported by the single metric have demonstrated its capability for calm and distress detection. Furthermore, 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 complementary outcomes. Finally, the combination of the three entropy metrics has considerably overcome the results ever reported by other indices in similar studies.