Association Between Phase Coupling of Respiratory Sinus Arrhythmia and Slow Wave Brain Activity During Sleep

Phase coupling of respiratory sinus arrhythmia (RSA) has been proposed to be an alternative measure for evaluating autonomic nervous system (ANS) activity. The aim of this study was to analyze how phase coupling of RSA is altered during sleep, in order to explore whether this measure is a predictor of slow wave sleep (SWS). Overnight electroencephalograms (EEG), electrocardiograms (ECG), and breathing using inductance plethysmography were recorded from 30 healthy volunteers (six females, age range 21–64, 31.6 ± 14.7 years). Slow wave activity was evaluated by the envelope of the amplitude of the EEG δ-wave (0.5–4 Hz). The RSA was extracted from the change in the R-R interval (RRI) by band-pass filter, where pass band frequencies were determined from the profile of the power spectral density for respiration. The analytic signals of RSA and respiration were obtained by Hilbert transform, after which the amplitude of RSA (ARSA) and the degree of phase coupling (λ) were quantified. Additionally, the normalized high-frequency component (HFn) of the frequency-domain heart rate variability (HRV) was calculated. Using auto- and cross-correlation analyses, we found that overnight profiles of λ and δ-wave were correlated, with significant cross-correlation coefficients (0.461 ± 0.107). The δ-wave and HFn were also correlated (0.426 ± 0.115). These correlations were higher than that for the relationship between δ-wave and ARSA (0.212 ± 0.161). The variation of λ precedes the onset of the δ-wave by ~3 min, suggesting a vagal enhancement prior to the onset of SWS. Auto correlation analysis revealed that the periodicity of λ was quite similar to that of the δ-wave (88.3 ± 15.7 min vs. 88.6 ± 16.3 min, λ-cycle = 0.938 × δ-cycle + 5.77 min, r = 0.902). These results suggest that phase coupling analysis of RSA appears to be a marker for predicting SWS intervals, thereby complementing other noninvasive tools and diagnostic efforts

Estimation of Melanin and Hemoglobin Using Spectral Reflectance Images Reconstructed from a Digital RGB Image by the Wiener Estimation Method

A multi-spectral diffuse reflectance imaging method based on a single snap shot of Red-Green-Blue images acquired with the exposure time of 65 ms (15 fps) was investigated for estimating melanin concentration, blood concentration, and oxygen saturation in human skin tissue. The technique utilizes the Wiener estimation method to deduce spectral reflectance images instantaneously from an RGB image. Using the resultant absorbance spectrum as a response variable and the extinction coefficients of melanin, oxygenated hemoglobin and deoxygenated hemoglobin as predictor variables, multiple regression analysis provides regression coefficients. Concentrations of melanin and total blood are then determined from the regression coefficients using conversion vectors that are numerically deduced in advance by the Monte Carlo simulations for light transport in skin. Oxygen saturation is obtained directly from the regression coefficients. Experiments with a tissue-like agar gel phantom validated the method. In vivo experiments on fingers during upper limb occlusion demonstrated the ability of the method to evaluate physiological reactions of human skin

Non-contact imaging of peripheral hemodynamics during cognitive and psychological stressors

Peripheral hemodynamics, measured via the blood volume pulse and vasomotion, provide a valuable way of monitoring physiological state. Camera imaging-based systems can be used to measure these peripheral signals without contact with the body, at distances of multiple meters. While researchers have paid attention to non-contact imaging photoplethysmography, the study of peripheral hemodynamics and the effect of autonomic nervous system activity on these signals has received less attention. Using a method, based on a tissue-like model of the skin, we extract melanin Cm and hemoglobin CHbO concentrations from videos of the hand and face and show that significant decreases in peripheral pulse signal power (by 36% +/- 29%) and vasomotion signal power (by 50% +/- 26%) occur during periods of cognitive and psychological stress. Via three experiments we show that similar results are achieved across different stimuli and regions of skin (face and hand). While changes in peripheral pulse and vasomotion power were significant the changes in pulse rate variability were less consistent across subjects and tasks

Estimation of Melanin and Hemoglobin Using Spectral Reflectance Images Reconstructed from a Digital RGB Image by the Wiener Estimation Method

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Development and exploration of a timbre space representation of audio

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