Spectral differences in resting-state EEG associated to individual Emotional Styles.

The ability to manage the emotions has been associated to the Emotional Styles (ES), a set of coherent ways to deal with life's experiences. Recently, the Emotional Style Questionnaire (ESQ) has been proposed as a self-report mea-sure to assess the individual ES. The present study investigates the spectral differences in the resting-state EEG due to the individual ES, in order to support the psychometric reliability of the ESQ with associated neurophysiological measurements. In the alpha and beta band, Social Intuition showed significant and large (d > 0.8) effect sizes on the parietal and parieto-occipital regions, as well as a significant and large effect size in the gamma band on the pre-frontal region. In the beta band, Attention showed a significant and large effect size on the parieto-occipital region.

It's a Question of Methods: Computational Factors Influencing the Frontal Asymmetry in Measuring the Emotional Valence.

The prefrontal asymmetry (FA) in the alpha band is a well-known physiological correlate of the emotional valence. Several methods for assessing the FA have been proposed in literature, but no studies have compared their effectiveness in a comprehensive way. In this study we first investigated whether the association between FA and valence depends on the computational methods and then, we identified the best one, namely the one giving the highest correlation with the self-reports. The investigated factors were the presence of a normalization factor, the computation in time or frequency domain and the cluster of electrodes used. All the analyses were implemented on the validated DEAP dataset. We found that the number and position of the electrodes do not influence the FA, in contrast with both the power computation method and the normalization. By using a spectrogram-based approach and by adding a normalization factor, a correlation of 0.36 between the FA and the self-reported valence was obtained.

Yellow (Lens) Better: Bioelectrical and Biometrical Measures to Assess Arousing and Focusing Effects.

Colours can induce several psychological effects, conditioning perceptions, cognitive/emotional states and human performances. In this exploratory study we investigated the effect of a yellow light exposure, obtained filtering the ambient light with coloured glasses, on the human's psychological functioning. In particular we wanted to assess if people are more able to focus when exposed to a yellow light. We recorded EEG, SC, HR and gaze-related data from 16 subjects (50% split in experimental and control group) during the execution of a reactivity test (the Hazard Perception Test, HPT). Compared with the control group, the experimental group showed increases in concentration, focus, visual attention and arousal, as measured by increases of first fixation duration and Beta over-Alpha ratio (BAR) as well as by decreases of distraction, workload, and number of gaze revisits.

Emotion assessment using Machine Learning and low-cost wearable devices.

The advancement in bioelectrical measurement technologies and the push towards a higher impact of the Brain Computer Interfaces and Affective Computing in the daily life have made non-invasive and low-priced devices available to the large population to record physiological states. The aim of this study is the assessment of the abilities of the MUSE headband, together with the Shimmer GSR+ device, to assess the emotional state of people during stimuli exposure. Twenty-four pictures from the IAPS database were showed to 54 subjects and were evaluated in their emotional values by means of the Self-Assessment Manikin (SAM). Using a Machine Learning approach, fifty-two scalar features were extracted from the signals and used to train 6 binary classifiers to predict the valence and arousal elicited by each stimulus. In all classifiers we obtained accuracies ranging from 53.6% to 69.9%, confirming that these devices are able to give information about the emotional state.

Application of the Ramanujan Fourier Transform for the analysis of secondary structure content in amino acid sequences.

OBJECTIVE: A novel method is presented for the investigation of protein properties of sequences using Ramanujan Fourier Transform (RFT). METHODS: The new methodology involves the preprocessing of protein sequence data by numerically encoding it and then applying the RFT. The RFT is based on projecting the obtained numerical series on a set of basis functions constituted by Ramanujan sums (RS). In RS components, periodicities of finite integer length, rather than frequency, (as in classical harmonic analysis) are considered. RESULTS: The potential of the new approach is documented by a few examples in the analysis of hydrophobic profiles of proteins in two classes including abundance of alpha-helices (group A) or beta-strands (group B). Different patterns are provided as evidence. CONCLUSIONS: RFT can be used to characterize the structural properties of proteins and integrate complementary information provided by other signal processing transforms.

Analysis of heart rate variability to predict patient age in a healthy population.

OBJECTIVES: To estimate age of healthy subjects by means of the heart rate variability (HRV) parameters thus assessing the potentiality of HRV indexes as a biomarker of age. METHODS: Long-term indexes of HRV in time domain, frequency domain and non-linear parameters were computed on 24-hour recordings in a dataset of 63 healthy subjects (age range 20-76 years old). Then, as interbeat dynamics markedly change with age, showing a reduced HRV in older subjects, we tried to capture age-related influence on HRV by principal component analysis and to predict the subject age by means of a feedforward neural network. RESULTS: The network provides good prediction of patient age, even if a slight overestimation in the younger subjects and a slight underestimation in the older ones were observed. In addition, the important contribution of non-linear indexes to prediction is underlined. CONCLUSIONS: HRV as a predictor of age may lead to the definition of a new biomarker of aging.

Assessment of beat-to-beat heart rate detection method using a camera as contactless sensor.

Video photoplethysmography (videoPPG) has emerged as area of great interest thanks to the possibility of remotely assessment of cardiovascular parameters, as heart rate (HR), respiration rate (RR) and heart rate variability (HRV). The present article proposes a fully automated method based on chrominance model, that selects for each subject the best region of interest (ROI) to detect and evaluate the accuracy of beat detection and interbeat intervals (IBI) measurements. The experimental recordings were conducted on 26 subjects which underwent a rest-to-stand maneuver. The results show that the accuracy of beat detection is slightly better during supine position (95%) compared to the standing one (92%), due to the maintenance of the balance that introduces larger motion artifact in the signal dynamic. The error in the measurement (expressed as mean±sd) of instantaneous heart rate is of +0.04 ±3.29 bpm in rest and +0.01±4.26 bpm in stand.

A method for coronary bifurcation centerline reconstruction from angiographic images based on focalization optimization.

A method for the reconstruction of a vessel centerline from angiographic images is outlined in this work. A typical coronary artery segment with bifurcations was emulated with a 3D printed static phantom and several angiograms were acquired at various angular positions on the C-Arm. The effectiveness of the reconstruction turned out to be largely influenced by the intrinsic parameters of the angiographic system, particularly the homogeneous coordinates system scaling factor λ. Therefore, recourse was made to a heuristic optimization method to estimate the optimal value of λ for each view. We measured the reliability of the reconstruction method by varying the fitness function of the optimization step and measuring the distances of 8 test points in comparison to the corresponding points identified in the µCT centerline. Preliminary results showed that, with an adequate number of views, the adoption of the optimal fitness function allowed the median distance error to be decreased below the acceptance threshold of 10%. As expected, the reliability of the method is improved by increasing the number of processed views.

Assessment of heart rate variability changes during dipyridamole infusion and dipyridamole-induced myocardial ischemia: a time variant spectral approach.

OBJECTIVES: We sought to evaluate changes in RR interval variability during dipyridamole infusion and dipyridamole-induced myocardial ischemia. BACKGROUND: Myocardial ischemia and the autonomic nervous system can be mutually interdependent. Spectral analysis of RR interval variability is a useful tool in assessing autonomic tone. METHODS: We used a time variant autoregressive spectral estimation algorithm that could extract spectral variables even in the presence of nonstationary signals. Two groups were considered: group A (patients with ischemia, n = 15) with effort or mixed angina, angiographically assessed coronary artery disease and positive exercise and dipyridamole echocardiographic test results, and group B (control subjects, n = 10) with normal exercise and dipyridamole echocardiographic test results. We investigated the following variables: RR interval mean and variance, low frequency (LF) and high frequency (HF) power in normalized units, LF ratio (LF/LFbasal power), HF ratio (HF/HFbasal power) and LF/HF ratio. For each test epoch, we calculated for group A and group B the mean value +/- SE of all indexes considered. Differences due to an effect either of group (ischemic vs. control) or of time (including both drug and ischemia effects) were analyzed by using analysis of variance for repeated measurements. RESULTS: Dipyridamole injection was characterized by a reduction of all spectral components in negative test. The LF ratio was the only variable able to discriminate patients with ischemia from control subjects (p < 0.05), whereas a time effect was evident for both mean RR interval and high frequency power in normalized units (p < 0.05). The LF ratio decreased in group B from 1 +/- 0.00 (basal) to 0.31 +/- 0.22 (peak), and increased in group A from 1 +/- 0.00 to 15.41 +/- 6.59, respectively. Results of an unpaired t test comparing the peak values of the two groups were also statistically significant (p < 0.01). CONCLUSIONS: Our data show that time variant analysis of heart rate variability evidences an increase in the low frequency ratio that allows differentiation of positive from negative test results, suggesting that the electrocardiogram may contain ischemia information unrelated to ST-T variations, even if their enhancement requires a more complex data processing procedure.

Quantification of ventricular repolarization heterogeneity during moxifloxacin or sotalol administration using [Formula: see text]-index.

Drug-induced alterations of ventricular heterogeneity must be limited to avoid induction of lethal ventricular arrhythmias. In here, a new parameter called [Formula: see text]-index, able to measure the standard deviation of myocites' repolarization times, was evaluated after moxifloxacin and sotalol administration. The two drugs are known to provide different alteration of the QT interval length ranging from subtle (moxifloxacin) to evident (sotalol). In fact, while the former is employed as active-comparator in thorough QT studies, the latter might induce torsades de pointes. 24 h Holter ECGs of 39 (sotalol) and 68 (moxifloxacin) healthy subjects were retrospectively analyzed. The recordings were performed after infusion of the drugs and after the placebo (moxifloxacin) or at baseline (sotalol). The corrected QT interval (QTc) was included as well in the study, for a direct comparison. In both populations, [Formula: see text]-index and QTc increased along with the drugs' serum concentration and were statistically different from values in the placebo arm or at baseline (p < 0.05).With sotalol, the maximum value of [Formula: see text]-index occurred, on average, after 5.64 h from the infusion, whereas for QTc after about 4.27 h. The two metrics displayed evident changes ([Formula: see text]-index: 27.79 ms ± 4.89 ms versus 60.13 ms ± 18.52 ms; QT corrected: 387.07 ms ± 19.84 ms versus 437.76 ± 32.05 ms; p < 0.05). Regarding moxifloxacin, maximum values were reached, on average, 5.01 h after administration for [Formula: see text]-index (30.70 ms ± 8.32 ms versus 40.48 ms ± 7.61 ms; p < 0.05), and 4.37 h for QTc (404.29 ms ± 29.05 ms versus 426.77 ± 36.67 ms; p < 0.05). They were statistically different from baseline values. With both drugs, the maximal percent variation after administration was higher for [Formula: see text]-index than QTc (moxifloxacin: 34.56% ± 24.60% versus 5.56% ± 2.98% ; sotalol: 114.77% ± 33.15% versus 12.13% ± 2.85% ; p < 0.05).The study suggests that the standard deviation of the ventricular repolarization times, as quantified by the [Formula: see text]-index, might be an effective measure of spatial heterogeneity.

Pole-tracking algorithms for the extraction of time-variant heart rate variability spectral parameters.

Various algorithms of autoregressive (AR) recursive identification make it possible to evaluate power spectral distribution in correspondence with each sample of a time series, and time-variant spectral parameters can be calculated through the evaluation of the pole positions in the complex z-plane. In traditional analysis, the poles are obtained by zeroing the denominator of the model transfer function, expressed as a function of the AR coefficients. In this paper, two algorithms for the direct updating and tracking of movements of poles of an AR time-variant model on the basis of the innovation given to the coefficients are presented and investigated. The introduced algorithms are based upon 1) the classical linearization method and 2) a recursive method to compute the roots of a polynomial, respectively. In the present paper, applications in the field of heart rate variability (HRV) signal analysis are presented and efficient tools are proposed for quantitative extraction of spectral parameters (power and frequency of the low-frequency (LF) and high-frequency (HF) components) for the monitoring of the action of the autonomic nervous system in transient patho-physiological events. These computational methods seem to be very attractive for HRV applications, as they inherit the peculiarity of recursive time-variant identification, and provide a more immediate comprehension of the spectral process characteristics when expressed in terms of poles and AR spectral components.

Multivariate time-variant identification of cardiovascular variability signals: a beat-to-beat spectral parameter estimation in vasovagal syncope.

In this paper a bivariate, time-variant model able to continuously measure the mutual interactions between heart rate and systolic blood pressure variability signals is presented. A recursive identification of the model parameters makes it possible to estimate, on a beat-to-beat basis, spectral low-frequency (LF) and high-frequency (HF) power, (LF/HF ratio) and cross-spectral (coherence and phase relationships between spectral peaks) indexes during nonstationary events. These indexes can be helpful in: 1) physiological study of autonomic nervous system mechanisms of cardiovascular control and 2) quantification and clinical evaluation of the neural and mechanical links between the two signals. In addition, an estimate of baroreceptive activation (alpha-gain) is continuously extracted. Before applying the model to cardiovascular signals, the reliability of the estimated parameters was tested on simulated signals. Subsequently, the model was applied to investigating vasovagal syncope episodes, aiming at the assessment of autonomic nervous system status and autonomic role in the dynamic phenomena which lead to syncope. The proposed model, which provides noninvasive beat-to-beat evaluation of the autonomic events, may be useful in the description of the syncopal episodes and in the comprehension of the complex physiological mechanisms of syncope.

Advanced spectral methods for detecting dynamic behaviour.

The traditional analysis in the frequency domain of cardiovascular variability signals requires stationarity along the considered temporal window, in order to obtain reliable indicators of the sympatho-vagal balance (low frequency (LF) and high frequency (HF) power and frequency, and LF/HF ratio). Through proper advanced algorithms of signal processing, it is possible to implement methods that allow the enhancement of important parameters about the behaviour of the system under investigation in the time and frequency domain. Both non-parametric and parametric time-frequency methods are generally employed at this purpose. Among them, Wigner-Ville Distribution and Time-Variant Autoregressive models are here described. Through such advanced methods of signal processing, it is possible to investigate the dynamic properties of the spectral parameters during transient physiological or pathological episodes, after a proper validation using simulated signals. The methods are used in various applicative areas of interest where the spectral parameters present a significant change in time and where the classical spectral analysis cannot be correctly applied. A few significant cases will be discussed such as tilting manoeuvre, vaso-vagal syncope onset and progression, and acute ischemic episodes. Further, multivariate analysis can be applied in which the focus is on squared coherence function and phase relationships, in order to estimate some possible causal effects in different experimental conditions. It is believed that such advanced methods of time-variant or time-frequency approaches are capable of overcoming the problem of stationarity in classical spectral analysis and to make applicable frequency domain techniques in the study of transient episodes which generally characterise various physiological and clinical conditions.

Clinical evaluation of algorithms for ST measurement during exercise test.

HYPOTHESIS: Computer processing of the exercise electrocardiogram (ECG) has many advantages, but the reliability of the analysis algorithms is not easily evaluable. No standard annotated database, nor recommended practice for testing and reporting performance results is available: thus, performance evaluation of such devices can be accomplished only by using a set of unannotated recordings, obtained in clinical practice. We evaluated the accuracy of an original microcomputer-based exercise test analyzer comparing the ST computer output with the measurements obtained by two experienced cardiologists. METHODS: Six hundred ECG strips were randomly selected from the exercise test recordings of 60 patients. The ST shift (at J + 80 ms) was blindly assessed by two observers (with the aid of a calibrated lens) and compared with computer measurements. Correlation coefficients, linear regression equations, percent of discrepant measurements, and 95% confidence limits of the mean error were calculated for all leads, peripheral leads, precordial leads, and "stress-test" leads (II, III, aVF, V4, V5, V6). RESULTS: The computer did not analyze five samples on a total of 600 (0.83%) ECG strips because of excessive noise or signal loss, while 51 (8.5%) were considered unreadable by both observers and 67 (11.2%) were rejected by at least one observer. Correlation between the measurements taken by computer and observer(s) measurements was statistically significant (p < 0.001 for all lead groups), no systematic measurement bias was found, and the mean difference was lower than human eye resolution. CONCLUSIONS: Our algorithms provide results as good as those provided by trained cardiologists in measuring ST changes occurring during exercise test. However, this study did not evaluate whether computer improvement of the signal-to-noise ratio would allow accurate measurements even on cardiologists' uninterpretable ECG. This potential advantage of computer-assisted analysis could be assessed only by using a dedicated exercise test database, in which different patterns of noise are superimposed on noise-free recordings previously annotated for ST level.

On-line beat-to-beat monitoring of spectral parameters of heart rate variability signal using a pole-tracking algorithm.

Spectral parameters extracted from the heart rate variability (HRV) signal are obtained on a beat-to-beat basis, following a procedure which uses two recursive algorithms. In the first step of the procedure the set of the AR model coefficients is updated each time a new RR value is available. Then from the estimated AR model parameters, the new position of the poles of the model transfer function in the complex z-plane is evaluated and, finally, through a residual calculation, it is possible to calculate the spectral parameters which quantify the control of the autonomic nervous system in assessing the cardiac frequency (i.e., power and frequency of LF and HF components). The whole procedure has first been tested on a simulated time series, in order to evaluate its performance in tracking the dynamic changes during different conditions; next the algorithms were employed in the study of the HRV signal for continuous monitoring of non-stationary conditions.

Automatic decomposition of Wigner distribution and its application to heart rate variability.

OBJECTIVE: We introduce an algorithm for the automatic decomposition of Wigner Distribution (WD) and we applied it for the quantitative extraction of Heart Rate Variability (HRV) spectral parameters during non-stationary events. Early response to tilt was investigated. METHODS: Quantitative analysis of multi-components non-stationary signals is obtained through an automatic decomposition of WD based on least square (LS) fitting of the instantaneous autocorrelation function (ACF). Through this approach the different signal and interference terms which contributes to the ACF may be separated and their parameters (instantaneous frequency and amplitude) quantified. A beat-to-beat monitoring of HRV spectral components is obtained. RESULTS: Analysis of simulated signals demonstrated the capability of the proposed approach to track and separate the signal components. Analysis of HRV data evidenced different dynamics in the early Autonomic Nervous System (ANS) response to tilt. CONCLUSIONS: The novel approach to the quantification of the beat-to-beat HRV spectral parameters obtained from decomposition of Wigner distribution was demonstrated to be effective in the analysis of HRV data. Relevant physiological information about the dynamics of the early sympathetic response to tilt were obtained. The method is a general approach which may be employed for a quantitative time-frequency analysis of non-stationary biological signals.

The whale forgetting factor in recursive AR spectral analysis of heart rate variability signals.

Spectral parameters extracted from the heart rate variability signal are obtained on a beat-to-beat basis by means of autoregressive recursive identification. In this paper a whale forgetting window is introduced, instead of the classical exponential one, in order to reduce the noise influence on the estimated parameters. After proper simulation it was found that the whale forgetting window markedly reduces the noise in the identification, but maintains a good response to abrupt changes in the signal. The algorithm was thus applied to the analysis of the HRV data recorded during different transient situations in physiological and pathological conditions. The spectral parameters were obtained on a beat-to-beat basis and their trends were smoother and more accurate with respect to the traditional exponential window also in presence of noise or artifacts in the time series (sudden and short time changes, ectopic beats, etc.), without losing the signal variations of physiological interest.

Event-related brain potentials: laplacian transformation for multichannel time-frequency analysis.

During a visual-motor task the movement strategies and the learning processes are investigated. A group of 10 normal young volunteers underwent the experiment. The EEG signal was recorded through the 10-20 acquisition system during the execution of a task after a visual input. Each subject repeated the movement several times in three different conditions: i) without knowledge of the performance; ii) with visual feedback; iii) with knowledge of the result. The signal was transformed through Laplacian operator in order to eliminate the spurious coherence and then time-variant coherence was calculated. Different trends of the coherence function have been evidenced in subjects learning and not learning the better movement strategy. In particular, relations have been found between frontal, central and occipital electrodes in medium and high frequency ranges.

Linear and non-linear analysis of atrial signals and local activation period series during atrial-fibrillation episodes.

Linear and non-linear indexes for the characterisation of the dynamics in atrial signals (AS) and local atrial period (LAP) series are assessed in different atrial fibrillation (AF) episodes as defined by Wells. Parameters include the linear index obtained from the cross-correlation function (CCF) between ASs and the non-linear synchronisation (S) index based on the mutual corrected conditional entropy (MCCE). Regularity (R) was computed on single-lead AS. In addition, the level of predictability (LP) and the regularity of LAP series were computed. It was found that the level of synchronisation between ASs decreased passing from type-I to type-II AF when using linear (CCF: 0.90 +/- 0.10 against 0.44 +/- 0.18; p<0.001) and non-linear (S: 0.22 +/- 0.10 against 0.05 +/- 0.03; p<0.001) indexes. The regularity index (in normal sinus rhythm (NSR): 0.30 +/- 0.08; in AF-I: 0.19 +/- 0.10; in AF-II: 0.09 +/- 0.02; NSR against AF-I p<0.001; AF-I against AF-II p<0.001) and level of predictability (in NSR: 65 +/- 18; in AF-I: 27 +/- 13; in AF-II 7 +/- 6; NSR against AF-I p<0.001; AF-I against AF-II p<0.001) significantly decreased in the LAP series passing from NSR to AF-II. The proposed parameters succeeded in discriminating the different dynamics which characterised AS and LAP series during different kinds of AF episodes.

Autonomic function and baroreflex sensitivity during angiotensin-converting enzyme inhibition or angiotensin II AT-1 receptor blockade in essential hypertensive patients.

OBJECTIVE: The influence of ACE-inhibition and angiotensin II ATI receptor blockade on the autonomic function and baroreflex sensitivity was investigated in hypertension. METHODS AND RESULTS: Heart rate variability was assessed in a resting condition by power spectrum analysis to evaluate the low frequency (LF) power, high frequency (HF) power and LF/HF ratio in 19 hypertensive patients and 23 normotensive controls. Moreover, the coherence between the tachogram and the systogram was evaluated, and the baroreflex gain (alphaLF-index), describing the transfer function of variability in the systolic pressure signal to variability in the RR interval, was obtained. Then a 24-h ambulatory blood pressure monitoring was performed. The 19 hypertensive patients were randomized to either enalapril or losartan treatment, and after 2 months were re-submitted to the RR variability and baroreflex study and to blood pressure monitoring. The subjects then crossed to the other antihypertensive treatment and were re-evaluated after an additional two months. No significant difference was found either in LF power and HF power and LF/HF ratio between normotensive and hypertensive subjects whereas a slight though significant difference was observed in the alphaLF-index. In hypertensive patients, both the treatments with enalapril and losartan reduced blood pressure and had no effect on heart rate. No significant change was observed in autonomic balance or in baroreflex sensitivity during the two antihypertensive treatments. CONCLUSIONS: In hypertensive patients, the angiotensin system or bradykinins do not seem to have any modulatory effect on the sympathetic/parasympathetic control of blood pressure and baroreflex sensitivity, in a resting condition. Since heart rates were unchanged by the two antihypertensive treatments despite a significant reduction of blood pressure, a resetting of baroreflex function was observed during both ACE-inhibition and angiotensin II ATI receptor blockade.