Assessing stress variations in children during the strange situation procedure: comparison of three widely used respiratory sinus arrhythmia estimation methods.

Objective.The respiratory sinus arrhythmia (RSA) is a well-known marker of vagal activity that can be exploited to measure stress changes. RSA is usually estimated from heart rate variability (HRV). This study aims to compare the RSA obtained with three widely adopted methods showing their strengths and potential pitfalls.Approach.The three methods are tested on 69 healthy preschoolers undergoing a stressful protocol, the strange situation procedure (SSP). We compare the RSA estimated by the Porges method, the univariate autoregressive (AR) spectral analysis of the HRV signal, and the bivariate AR spectral analysis of HRV and respirogram signals. We examine RSA differences detected across the SSP episodes and correlation between the estimates provided by each method.Main results.The Porges and the bivariate AR approaches both detected significant differences (i.e. stress variations) in the RSA measured across the SSP. However, the latter method showed higher sensitivity to stress changes induced by the procedure, with the mean RSA variation between baseline and first separation from the mother (the most stressful condition) being significantly different among methods: Porges, -17.5%; univariate AR, -18.3%; bivariate AR, -23.7%. Moreover, the performances of the Porges algorithm were found strictly dependent on the applied preprocessing.Significance.Our findings confirm the bivariate AR analysis of the HRV and respiratory signals as a robust stress assessment tool that does not require any population-specific preprocessing of the signals and warn about using RSA estimates that neglect breath information in more natural experiments, such as those involving children, in which respiratory frequency changes are extremely likely.

In vitro study on conditioned dental root surfaces: evaluation of wettability, smear layer, and blood clot adhesion.

OBJECTIVES: The aim was to investigate the efficacy of citric acid and ethylenediaminetetraacetic acid (EDTA)-based treatments on smear layer removal and blood clot formation and stabilization. METHOD AND MATERIALS: After scaling and root planing, 126 root samples were divided into seven groups treated with: deionized water; saline; citric acid solution; Ultradent Citric Acid gel; EDTA solution; EDTA-based PrefGel; or untreated. Each group was divided into three subgroups: I for the evaluation of smear layer removal and surface wettability, II and III for the evaluation of blood clot formation and stabilization in static or dynamic rinsing conditions. RESULTS: Conditioning agent treatments increased surface wettability with respect to untreated samples (Ultradent: 45 ± 1 degrees, P = 6.2 × 10-3; EDTA: 36 ± 5 degrees, P = 8.9 × 10-7; PrefGel: 47 ± 7 degrees, P = 3.2 × 10-2). Smear layer removal (30% to 60% with respect to untreated samples) was observed for all the conditioning agents. Clot was absent on untreated samples and samples treated with deionized water. Clot quality was significantly higher for samples treated with conditioning agents (P < .05) and similar between group II and III (P > .05). A statistically significant difference (P = .027) was observed for clot coverage of the saline group, comparing static and dynamic rinsing, confirming the positive effect of treatments on clot stabilization. CONCLUSIONS: The use of conditioning agents improves smear layer removal and clot formation and stabilization with respect to scaling and root planing procedure only, which is, however, considered an essential procedure to promote wound healing in periodontal surgery.

Exploration of the physiological response to an online gambling task by frequency domain analysis of the electrodermal activity.

Online gambling has dramatically increased over the last decades, thus the study of the underlying physiological mechanisms could be helpful to better understand related disorders. Specifically, physiological arousal is well-known to play a key role in gambling behavior. In the present study, unconventional frequency feature of the electrodermal activity (EDA) was extracted (EDASympn) and compared to the most common heart rate variability (HRV) spectral parameters (LF, HF, HFn, LF/HF) to measure arousal during an online gambling session. 46 subjects played online slot machines for 30 minutes, while EDA and ECG were recorded. In the analysis the gaming session was divided into three 10-minutes-long phases. A one-way repeated measures analysis of variance was carried out for each spectral parameter, with the game phases as within-subjects factor. All the calculated parameters showed significant differences between the initial phase of the game and the last two (p < 0.001). In particular, EDAsympn displayed a reciprocal trend with respect to HFn: an initial increase (decrease for HFn) was followed by a plateau phase. LF exhibited a significant difference also between the second and the third phases. EDA frequency-domain analysis appears to be a promising method for physiological arousal assessment, by showing the same discriminative power of HRV spectral components. Further research is needed to emphasize these findings.Clinical Relevance-This promotes the use of a new and easy-to-implement method to assess sympathetic activity.

Bicoherence Interpretation in EEG Requires Signal to Noise Ratio Quantification: An Application to Sensorimotor Rhythms.

OBJECTIVE: In the electroencephalogram (EEG) the quadratic phase coupling (QPC) phenomenon indicates the presence of non-linear interactions among brain rhythms that could affect the interpretation of their physiological meaning. We propose the use of the bicoherence as a QPC quantification method to understand the nature of brain rhythm interplay. METHODS: We firstly provide a simulation study to show under which condition of signal to noise ratio (SNR) the bicoherence is a reliable QPC quantifier and how to interpret the results. Secondly, in the light of the simulation results, we applied the bicoherence analysis to real EEG data acquired on thirteen volunteers during a cue-paced reaching motor task to quantify coupling and decoupling between mu and beta rhythms. An inter-trial averaging procedure was adopted in order to allow the correct calculation of the bicoherence during a motor task. RESULTS: Simulations demonstrated that SNR has a strong impact on the correct quantification of bicoherence and that a reliable detection of QPC is possible when the SNR is favorable (>-5 dB). Results from EEG data demonstrated a QPC between mu and beta rhythms during the resting state and its fading during movement planning and execution, providing valuable information for the interpretation of their dynamics. CONCLUSION: The bicoherence was proven to be an effective tool for the investigation of coupling between the sensorimotor rhythms during all the phases of a motor task. This was assessed in relation to the physiological changing of the SNR characterizing the frequency components of interest.

Higher order spectral analysis of scalp EEG activity reveals non-linear behavior during rhythmic visual stimulation.

OBJECTIVE: Flickering visual stimulation is known to evoke rhythmic oscillations in the electroencephalographic (EEG) activity, called steady-state visually evoked potentials (SSVEP). The presence of harmonic components in the EEG signals during SSVEP suggests the non-linearity of the visual-system response to rhythmic stimulation, but the nature of this behavior has not been deeply understood. The aim of this study is the quantitative evaluation and characterization of this non-linear phenomenon and its interference with the physiological alpha rhythm by means of spectral and higher order spectral analysis. APPROACH: EEG signals were acquired in a group of 12 healthy subjects during a pattern-reversal stimulation protocol at three different driving frequencies (7.5 Hz, 15 Hz and 24 Hz). Spectral power values were estimated, after Laplacian spatial filtering, to quantitatively evaluate the changes in the power of the individual alpha and stimulation frequencies related harmonic components. Bicoherence measure were employed to assess the presence of quadratic phase coupling (QPC) at each channel location. MAIN RESULTS: Our analysis confirmed a strong non-linear response to the rhythmic stimulus principally over the parieto-occipital channel locations and a simultaneous significant alpha power suppression during 7.5 Hz and 15 Hz stimulation. A prominent sub-harmonic component characterized the resonance behavior of the 24 Hz stimulation. SIGNIFICANCE: The findings presented suggest that bicoherence is a useful tool for the identification of QPC interactions between stimulus-related frequency components within the same signal and the characterization of the non-linearity of SSVEP-induced harmonics generation. In addition, the applied methodology demonstrates the presence of coupled EEG rhythms (harmonics of the main oscillation) both in resting condition and during stimulation, with different characteristics in the distinct brain areas.

Heart Rate Variability from Wearables: A Comparative Analysis Among Standard ECG, a Smart Shirt and a Wristband.

Heart Rate Variability (HRV) derived from standard one-lead electrocardiography (ECG) was compared with HRV computed by a commercial ECG shirt and with the inter-beat-intervals (IBI) measured by a research-grade photoplethysmographic (PPG) wristband. Signals from 8 subjects were recorded in three experimental phases: during sit; in upright position ("stand"); during controlled respiration. HRV and IBI from both the wearables were computed online (i.e. during the experiment) and stored for subsequent analyses, while the standard ECG was processed offline with state-of-the-art methods to obtain a clean reference HRV. Shirt and wristband signals accuracies were assessed, with respect to the reference HRV, through a between-phase and a beat-to-beat analyses. The former considered several time and frequency domain parameters; the latter was carried out through the Bland-Altman method. Time and frequency domain parameters computed from shirt HRV resulted very similar to the ones extracted from the reference HRV and generally more accurate than the parameters computed from wristband IBI. The Bland-Altman analysis showed that wristband IBI is significantly different from ECG-derived HRV, especially during "stand". Therefore, our results support the idea that some care should be paid in the interpretation of online PPG-derived IBI, while HRV measures online-derived from ECG-shirts seem to be more reliable in the analyzed conditions. The high number of missing beats also suggest that the design of wristband devices should be taken into account to reduce the rate of incorrect measurements, by maximizing sensor adhesion to the skin.

EEG Analysis During Active and Assisted Repetitive Movements: Evidence for Differences in Neural Engagement.

Two key ingredients of a successful neuro-rehabilitative intervention have been identified as intensive and repetitive training and subject's active participation, which can be coupled in an active robot-assisted training. To exploit these two elements, we recorded electroencephalography, electromyography and kinematics signals from nine healthy subjects performing a 2×2 factorial design protocol, with subject's volitional intention and robotic glove assistance as factors. We quantitatively evaluated primary sensorimotor, premotor and supplementary motor areas activation during movement execution by computing event-related desynchronization (ERD) patterns associated to mu and beta rhythms. ERD patterns showed a similar behavior for all investigated regions: statistically significant ERDs began earlier in conditions requiring subject's volitional contribution; ERDs were prolonged towards the end of movement in conditions in which the robotic assistance was present. Our study suggests that the combination between subject volitional contribution and movement assistance provided by the robotic device (i.e., active robot-assisted modality) is able to provide early brain activation (i.e., earlier ERD) associated with stronger proprioceptive feedback (i.e., longer ERD). This finding might be particularly important for neurological patients, where movement cannot be completed autonomously and passive/active robot-assisted modalities are the only possibilities of execution.

Optimization of time-variant autoregressive models for tracking REM-non REM transitions during sleep.

The aim of this study was the optimization of Time-Variant Autoregressive Models (TVAM) for tracking REM-non REM transitions during sleep, through the analysis of spectral indexes extracted from tachograms. A first improvement of TVAM was achieved by choosing the best typology of forgetting factor in the analysis of a tachogram obtained during a sitting-to-standing test; then, a method for improving robustness of AR recursive identification with respect to outliers was selected by analyzing a tachogram with an ectopic beat. A variable forgetting factor according to the Fortescue method and a specific condition on the prediction error for recursive AR identification gave the best performances. The optimized TVAM was then employed in the analysis of tachograms derived from ECGs recorded during a whole night, through a sensorized T-shirt, from 9 healthy subjects. The spectral indexes (power of tachogram in the LF and HF bands, LF/HF ratio and the absolute value of the spectrum pole in the HF band) were computed from the estimated AR parameters on a beat-to-beat basis. A two groups T-test aimed at comparing values assumed by each spectral index in REM and non-REM sleep epochs was performed. Significant statistical differences (p-value < 0.05) were found in three of the four spectral indexes computed. In conclusion, the combination of the Fortescue variant and of the robustness method based on the prediction error in the TVAM seems to be helpful in the differentiation between REM and non-REM sleep stages.

Clinical state assessment in bipolar patients by means of HRV features obtained with a sensorized T-shirt.

The aim of this study is to identify parameters extracted from the Heart Rate Variability (HRV) signal that correlate to the clinical state in patients affected by bipolar disorder. 25 ECG and activity recordings from 12 patients were obtained by means of a sensorized T-shirt and the clinical state of the subjects was assessed by a psychiatrist. Features in the time and frequency domain were extracted from each signal. HRV features were also used to automatically compute the sleep profile of each subject by means of an Artificial Neural Network, trained on a control group of healthy subjects. From the hypnograms, sleep-specific parameters were computed. All the parameters were compared with those computed on the control group, in order to highlight significant differences in their values during different stages of the pathology. The analysis was performed by grouping the subjects first on the basis of the depression-mania level and then on the basis of the anxiety level.