Heart rate variability analysis during hypnosis using wavelet transformation

Impact of hypnosis on psychophysiological measures: A scoping literature review

Exploring psychophysiological changes during hypnosis can help to better understand the nature and extent of the hypnotic phenomenon by characterizing its influence on the autonomic nervous system (ANS), in addition to its central brain effects. Hypnosis is thought to induce a relaxation response, yet studies using objective psychophysiological measures alongside hypnosis protocols show various results. We review this literature and clarify the effects of hypnosis on psychophysiological indices of ANS activity and more specifically of the stress/relaxation response, such as heart rate variability and electrodermal activity. Studies reporting psychophysical measures during hypnosis were identified by a series of Pubmed searches. Data was extracted with an interest for the influence of hypnotizability and effects of specific suggestions or tasks on the findings. We found 49 studies comprising 1315 participants, 45 concerning healthy volunteers and only 4 on patients. Sixteen compared high vs. low hypnotizable people; 30 measured heart rate, 18 measured heart rate variability, 25 electrodermal activity, and 23 respiratory signals as well as other physiological parameters. Globally, results converge to show reductions in sympathetic responses and/or increases in parasympathetic tone under hypnosis. Several methodological limitations are underscored, such as older studies (N = 16) using manual analyses, small sample sizes (<30, N = 31), as well as uncontrolled multiple comparisons. Nevertheless, we confirm that hypnosis leads to a physiological relaxation response and highlight promising avenues for this research. Suggestions are made for guiding future work in this field.

Comparison of the Use of Blink Rate and Blink Rate Variability for Mental State Recognition

Recent research has unearthed that blink rate variability (BRV) can be employed as a psychophysiological measure. However, its efficiency for mental state recognition (MSR) has not been investigated yet. Because BRV can indicate dynamics inherent in eye blinks, we conjectured that BRV might exhibit stronger abilities for the MSR if compared with blink rate (BR), known as the leading indicator derived from eye blinks for MSR. Therefore, in this paper, we attempted to differentiate between high and low cognitive loads of an individual through the analyses of BR and BRV, respectively, which could be viewed as a preliminary study for comparing their MSR abilities. First, an n -back experiment was performed to collect data. Then, in order to characterize the phenomenon of BRV, the features were extracted from its time and frequency domains, respectively. Finally, the area under the curve (AUC) values of BRV and BR for MSR were estimated by the ten commonly used classifiers, respectively. The results indicated that BRV achieves significantly higher AUC values than BR, which suggests its strong potentiality for MSR. In sum, the BRV may prove to be a promising method for the MSR, which should be considered in the future.