Dynamic Hilbert warping, a new measure of RR-interval signals evaluated in the cognitive load estimation

RR interval (RRI) signals represent the time intervals between successive heart R-waves. These signals are influenced by many cognitive and psychological processes. In this study, a new technique based on the combination of empirical mode decomposition and dynamic Hilbert warping (DHW) was proposed to inference cognitive states from measured RRI signals. Moreover, a set of entropic and statistical measures was extracted to characterize the regularity and temporal distribution in the phase spectra and amplitude envelope of the analytic signals. The discriminating capability of the proposed method was studied in 45 healthy subjects. They performed an arithmetic task with five levels of difficulty. The study indicated the importance of phase information in cognitive load estimation (CLE). The new phase characteristics were able to extract hidden information from the RRI signals. The results revealed a striking decrease in DHW value with increasing load level. The entropic measures of analytic signal also showed an increasing trend as the mental load increased. Although, phase information had an ability to discriminate between more distinct levels as well as between more similar ones, amplitude information was effective only in discriminating between more distinct levels.