Mood states modulate complexity in heartbeat dynamics: A multiscale entropy analysis

Phil. Trans. R. Soc. A.

Greco

Gentili

et al. 2016

Self Cite

Section: Discussionmentioning

confidence: 80%

Combining electroencephalographic activity and instantaneous heart rate for assessing brain–heart dynamics during visual emotional elicitation in healthy subjects

Phil. Trans. R. Soc. A.

Greco

Gentili

et al. 2016

Self Cite

“…However, other authors documented an increase in complexity of systolic arterial pressure in PD patients when compared to controls [32]. In addition, while the physiology underlying complexity-related measures of heartbeat dynamics is still unknown, it is well known that their quantification can provide meaningful information on psychophysiological as well as pathological states [8,10,14,24,39]. In this context, we have previously shown that measures of time-varying complexity provide enhanced discriminatory power when compared to standard complexity measures [36,38], and that the stability and complexity of autonomic dynamics is altered in PD patients when compared to healthy controls as well as within different PD subgroups [4,6].…”

Section: Introductionmentioning

confidence: 99%

Assessment of spontaneous cardiovascular oscillations in Parkinson's disease

Biomedical Signal Processing and Control

Orsolini

Diciotti

et al. 2016

Parki so 's disease PD has ee reported to i ol e postga glio i s patheti failure and a wide spectrum of autonomic dysfunctions including cadiovascular, sexual, bladder, gastrointestinal and sudo-motor abnormalities. While these symptoms may have a significant impact on daily activities, as well as quality of life, the evaluation of autonomic nervous system (ANS) dysfunctions relies on a large and expensive battery of autonomic tests only accessible in highly specialized laboratories. In this paper we aim to devise a comprehensive computational assessment of disease-related heartbeat dynamics based on instantaneous, time-varying estimates of spontaneous (resting state) cardiovascular oscillations in PD. To this end, we combine standard ANS-related heart rate variability (HRV) metrics with measures of instantaneous complexity (dominant Lyapunov exponent and entropy) and higher-order statistics (bispectra). Such measures are computed over 600-s recordings acquired at rest in 29 healthy subjects and 30 PD patients. The only significant group-wise differences were found in the variability of the dominant Lyapunov exponent. Also, the best PD vs. healthy controls classification performance (balanced accuracy: 73.47%) was achieved only when retaining the time-varying, non-stationary structure of the dynamical features, whereas classification performance dropped significantly (balanced accuracy: 61.91%) when excluding variability-related features. Additionally, both linear and nonlinear model features correlated with both clinical and neuropsychological assessments of the considered patient population.Our results demonstrate the added value and potential of instantaneous measures of heartbeat dynamics and its variability in characterizing PD-related disabilities in motor and cognitive domains.3

“…Future developments of devices might include other affective information gathered from the autonomic nervous system dynamics (e.g. [21]), to be also used for clinical applications [22], [23] involving patients with mood or emotional disorders, such as bipolar disorder (e.g., application in [24], [25]). …”

Section: Conclusion and Discussionmentioning

confidence: 99%

Electroencephalographic spectral correlates of caress-like affective haptic stimuli

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Greco

Nardelli

et al. 2015

This paper describes how brain dynamics, as estimated through spectral analysis of electroencephalographic (EEG) oscillatory rhythms, is modified by quantifiable, affective haptic stimuli. Specifically, 32 healthy subjects (16 females) interacted with a haptic device able to convey caress-like stimuli while varying force and velocity of the device itself. More specifically, 2 values of force (i.e., "strength of the caress") and 3 velocity levels (i.e. "velocity of the caress") were combined to control the device during the experiment. Subjects were also asked to self-assess the haptic stimuli in terms of arousal (activation/ deactivation) and valence (pleasure/displeasure) scores. Results, shown in terms of p-values topographic maps, revealed a suppression of the oscillations over the controlateral somatosensory cortex, during caresses performed with the lowest force (2N) and the highest velocity (65 mm/s). This occurred in all of the frequency bands considered, α, β, and γ. Lower velocities (9.4 mm/s and 37 mm/s) did not significantly modify EEG reactivity in such bands. Concerning caresses administered at high force (6N), there was a significant decrease of EEG oscillatory activity focused on mid-frontal electrodes, in all of the considered frequency bands, when the velocity of the caresses was the lowest one. Significant sparse decrease of EEG power spectra, in all of the considered frequency bands, occurred at higher strength and velocity of the caress.