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Human studies suggest that cardiovascular neural sympathetic control is predominantly modulated by the right cerebral hemisphere. It is unknown whether post-ganglionic sympathetic activity [muscle sympathetic nerve activity (MSNA)] shows any functional asymmetry. Eight right-handed volunteers (3 women and 5 men, 32 [+ or -] 2 yr of age) underwent ECG, beat-by-beat blood pressure, respiratory activity, and simultaneous right and left MSNA recordings during spontaneous and controlled breathing (CB, 15 breaths/rain, 0.25 Hz). Dynamic carotid baroreceptor stimulation was obtained by 0.1-Hz sinusoidal suction, from 0 to -50 mmHg, randomly applied to the right, left, and combined right and left sides of the neck during CB. Laterality was assessed by changes in the MSNA burst rate (in bursts/min, and bursts/100 beats), strength [amplitude (A) and area (AA)], and the oscillatory component at 0.1 Hz during baroreceptor stimulation. Amplitude parameters were normalized by CB burst mean amplitude and area of the same side. At rest, the right and left MSNA burst rate and total MSNA activity were similar. Conversely, the right MSNA normalized burst AN (1.36 [+ or -] 0.18) and [AA.sub.N] (1.31 [+ or -] 0.16) were larger than the left MSNA [A.sub.N] (1.04 [+ or -] 0.09) and [AA.sub.N] (1.02 [+ or -] 0.08). Unilateral and bilateral carotid baroreflex stimulation abolished the right prevalence of [A.sub.N] and [AA.sub.N]. In conclusion, the right lateralization of sympathetic activity to the vessels is indicated by normalized burst strength parameters of bilateral MSNA recordings at rest during spontaneous breathing. Carotid baroreceptor stimulation disrupted such expression of MSNA lateralization possibly by disturbing the synchronizing action of right cerebral hemisphere. sympathetic control of circulation; muscle sympathetic nerve activity recording; burst amplitude; area; laterality

In this paper, an analysis of heart rate variability (HRV) during decreases in the amplitude fluctuations of photopletysmography (PPG) [decreases in the amplitude fluctuations of photopletysmography (DAP)] events for obstructive sleep apnea syndrome (OSAS) screening is presented. Two hundred and sixty-eight selected signal segments around the DAP event were extracted and classified in five groups depending on Sa[O.sub.2] and respiratory behavior. Four windows around each DAP are defined and temporal evolution of time--frequency HRV parameters was analyzed for OSAS screening. Results show a significant increase in sympathetic activity during DAP events, which is higher in cases associated with apnea. DAP events were classified as apneic or nonapneic using a linear discriminant analysis from the HRV indexes. The ratio of DAP events per hour [r.sub.DAP] and the ratio of apneic DAP events per hour [r.sup.a.sub.DAP] were computed. Results show an accuracy of 79% for [r.sup.a.sub.DAP] (12% increase with respect to [r.sub.DAP]),a sensitivity of 87.5%, and a specificity of 71.4% when classifying 1-h polysomnographic excerpts. As for clinical subject classification, an accuracy of 80% (improvement of 6.7%), a sensitivity of 87.5%, and a specificity of 71.4% are reached. These results suggest that the combination of DAP and HRV could be an improved alternative for sleep apnea screening from PPG with the added benefit of its low cost and simplicity. Index Terms--Children, decreases in the amplitude fluctuations of photopletysmography (PPG), heart rate variability (HRV), pulse PPG, sleep apnea, time--frequency.

We present a new method for rejecting noise from HPLC-MS data sets. The algorithm reveals peptides at low concentrations by minimizing both the chemical and the random noise. The goal is reached through a systematic approach to characterize and remove the background. The data are represented as two-dimensional maps, in order to optimally exploit the complementary dimensions of separation of the peptides offered by the LC-MS technique. The virtual chromatograms, reconstructed from the spectrographic data, have proved to be more suitable to characterize the noise than the raw mass spectra. By means of wavelet analysis, it was possible to access both the chemical and the random noise, at different scales of the decomposition. The novel approach has proved to efficiently distinguish signal from noise and to selectively reject the background while preserving low-abundance peptides.

A method for on-line single sweep detection of somatosensory evoked potentials (SEPs) during intraoperative neuromonitoring is proposed. It is based on a radial-basis function neural network with Gaussian activations. In order to improve its tracking capabilities, the radial-basis functions location is partially learnt sweep-by-sweep; the training algorithm is effective, though consistent with real-time applications. This new detection method has been tested on simulated data so as to set the network parameters. Moreover, it has been applied to real recordings obtained from a new neuromonitoring technique which is based on the simultaneous observation of the SEP and of the evoked H-reflex elicited by the same electric stimulus. The SEPs have been extracted using the neural network and the results have then been compared to those obtained by ARX filtering and correlated with the spinal cord integrity information obtained by the H-reflex. The proposed algorithm has been proved to be particularly effective and suitable for single-sweep detection. It is able to track both sudden and smooth signal changes of both amplitude and latency and the needed computational time is moderate. Index Terms--EP, H-reflex, neural network, neuromonitoring, RBFNN, SEP, single sweep, single trial.

This paper considers the multiscale entropy (MSE) approach for estimating the regularity of time series at different scales. Sample entropy (SampEn) and approximate entropy (ApEn) are evaluated in MSE analysis on simulated data to enhance the main features of both estimators. We applied the approximate entropy and the sample entropy estimators to fetal heart rate signals on both single and multiple scales for an early identification of fetal sufferance antepartum. Our results show that the ApEn index significantly distinguishes suffering from normal fetuses between the 30th and the 35th week of gestation. Furthermore, our data shows that the MSE entropy values are reliable indicators of the fetal distress associated with the presence of a pathological condition at birth. Index Terms--Cardiotocography, fetal heart rate, fetal pathologies, nonlinear parameters, spectral analysis.

Analysing the Holter recordings collected at baseline during the European Myocardial Infarction Amiodarone Trial (EMIAT), we evaluate the possibility of using [alpha], the slope of the power spectrum of heart rate variability signals (HRV) in the vicinity of f = 0, for postinfarction risk stratification. We found no relevant difference in the values of [alpha] for the placebo population. On the contrary, in the amiodarone arm, the distinction in the survival rates of those with high or low [alpha]-values was highly significant. Moreover, high risk patients with respect to [alpha] (higher values) did not seem to benefit from amiodarone. The results suggest that [alpha] might convey physiologic information that is different than what is expressed by other HRV characteristics, such as the triangular index. When combining high risk patients in term of triangular index ( Index Terms--Heart rate variability, scaling exponent, signal processing, spectral analysis.