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In this study, a semi-automatic, easy-to-use classification method for the identification and removal of fMRI noise is proposed and tested. The method relies on subject-level spatial independent component analysis (ICA) of fMRI data. Starting from a reference set of labeled independent components (ICs), novel ICs are classified as physiological/artefactual by combining a spatial correlation (SC) analysis with the reference ICs and relative power spectral (PS) analysis. Here, ICs from a task-based fMRI dataset were used as reference. SC and SP thresholds were set using a test dataset (5 subjects, same fMRI protocol) based on Receiving Operating Characteristic curves. The tool performance and versatility were measured on a resting-state fMRI dataset (5 subjects). Our results show that the method can automatically identify noise-related ICs with accuracy, specificity and sensitivity higher than 80% across different fMRI protocols. These findings also suggest that the reference set provided in the present study might be used to mark ICs coming from independent taskrelated or resting-state fMRI datasets.Clinical relevance- The new method will be included in a userfriendly, open-source tool for removal of noisy contributions from fMRI datasets to be used in clinical and research practices.

The use of questionnaires at the end of a specific task only evaluates what is expressed by the conscious mind and, therefore, cannot give a complete representation of the individual's emotional state. By adding data from physiological measures, such as cerebral activity, skin conductance, heart rate and gaze position, more accurate information about cognitive engagement and emotional responses to a given task could be provided. This study aims to evaluate participants' emotional (arousal, valence) and cognitive (memorization effort, attention, pleasantness) responses toward two videos, through the integration of above cited measures. Our findings show that the two tested videos produce two different unconscious reactions (one video causes a significantly higher increase in heart rate and the other one requires higher memorization effort), while producing similar conscious responses (no statistically significant differences were found by analyzing questionnaires' answers). Further, eye tracking device provided a way to investigate reasons behind these differences. The results show that the integration of self-reported and biological measures with eye tracking data could effectively help to understand emotional and cognitive responses during video observation.

Cardiac rhythm classification is usually achieved using the raw electrocardiogram signal (EKG), which is not always available. By means of dynamical measures we developed a RR based classifier which is able to distinguish normal sinus rhythm (NSR), atrial fibrillation (AF) and sinus rhythm with ectopy with an accuracy of 99%, 81% and 77%, respectively, using 10-minute segments. The classifier was built on the University of Virginia (UVa) Holter database.

This work aims to investigate sleep microstructure as expressed by Cyclic Alternating Pattern (CAP), and its possible alterations in pathological sleep. Three groups, of 10 subjects each, are considered: a) normal sleep, b) psychophysiological insomnia, and c) sleep misperception. One night sleep PSG and sleep macro- micro structure annotations were available per subject. The statistical properties and the dynamics of CAP events are in focus. Multiscale and non-linear methods are presented for the analysis of the microstructure event time series, applied for each type of CAP events, and their combination. The results suggest that a) both types of insomnia present CAP differences from normal sleep related to hyperarousal, b) sleep misperception presents more extensive differences from normal, potentially reflecting multiple sleep mechanisms, c) there are differences between the two types of insomnia as regard to the intertwining of events of different subtypes. The analysis constitutes a contribution towards new markers for the quantitative characterization of insomnia, and its subtypes.

Here we present a method for classifying fMRI independent components (ICs) by using an optimized algorithm for the individuation of noisy signals from sources of interest. The method was applied to estimate brain activations from combined EEG-fMRI data for the exploration of epilepsy. Spatial ICA was performed using the above-mentioned optimized algorithm and other three popular algorithms. ICs were sorted considering the value: of the coefficients of determination R2, obtained from the multiple regression analysis with morphometric maps of cerebral matter; of the kurtosis, which features the signal energy. The validation of the method was performed comparing the brain activations obtained with those resulted using the General Linear Model (GLM). The ICA-derived activations in different datasets comprised subareas of the GLM-revealed activations, even if the volume and the shape of activated areas do not correspond exactly. The method proposed also detects additional negative regions implicated in a default mode of brain activity, and not clearly identified by GLM. Compared with a traditional GLM approach, the ICA one provides a flexible way to analyze fMRI data that reduces the assumptions placed upon the hemodynamic response of the brain and the temporal constrains.

This study assesses complexity of cardiovascular control in patients affected by type-1 variant of long QT(LQT1) syndrome. Complexity was assessed by refined multiscale entropy of heart period (HP) and QT interval variabilities. HP was taken as the time distance between two consecutive R peaks (RR) and QT interval was approximated as the time distance between the R-peak and T-wave apex (RTa) and between R-peak and T-wave end (RTe). RR, RTa and RTe intervals were automatically extracted from 24h Holter recordings and the daytime period was analyzed (from 02:00 to 06:00 PM). Non mutation carrier (NMC) individuals (n=11), utilized as a control group, were taken from the same family line of the mutation carrier (MC) subjects (n=26). We found that, while NMC and MC groups were indistinguishable based on time domain and complexity analyses of RR dynamics, complexity analysis of RTa and RTe variabilities clearly separates the two populations and suggests an impairment in the cardiac control mechanisms acting on the ventricles.

The main objective of this work is to develop and apply data mining methods for the prediction of patient outcome in nephrology care. Cardiovascular events have an incidence of 20% in the first year of hemodialysis (HD). Real data routinely collected during HD administration were extracted from the Fresenius Medical Care database EuCliD (39 independent variables) and used to develop a random forest predictive model for the forecast of cardiovascular events in the first year of HD treatment. Two feature selection methods were applied. Results of these models in an independent cohort of patients showed a significant predictive ability. Our better result was obtained with a random forest built on 6 variables only (AUC: 77.1% ± 2.9%; MCE: 31.6% ± 3.5%), identified by the variable importance out of bag (OOB) estimate.

Congenital heart defects affect the efficiency and functionality of the heart, and autonomic control of heart rate and of circulation can display a pathologic behavior in order to compensate for the hemodynamic alterations due to disease. While previous works have investigated heart rate variability (HRV) in specific pathologies, e.g. tetralogy of Fallot, the goal of this study was to assess HRV in children with a congenital heart malformation taking into account the effects of cyanotic and acyanotic defects, and comparing pathologic children with age matched controls. HRV, approximate entropy (ApEn) and sample entropy (SampEn) were calculated to discuss the dynamics and complexity of heart rhythms, and to evaluate the potential impairment of control mechanisms. Analyses showed that low frequency (LF) power and total power of HRV were significantly higher in children with a condition than in healthy controls, independently of cyanosis. Non linear indices were also significantly higher in pathologic subjects. Significant differences in LF, total power, ApEn and SampEn were found among cyanotic, acyanotic and healthy children. These results suggested that children with a congenital heart condition display more complex HRV and sympathetic overactivity, which may be aimed at compensating for hemodynamic alterations. Further studies should investigate whether corrective surgery and rehabilitation can improve HRV and restore its physiological features.

This paper presents the analysis of autonomic nervous system (ANS) control of heart rate (HR) and of cardiac baroreflex sensitivity (BRS) in patients undergoing general anesthesia for major surgery through spectral analysis techniques and with the Granger causality approach that take into account the causal relationships between HR and arterial blood pressure (ABP) variability. Propofol produced a general decrease in ABP due to its vasodilatory effects, a reduction in BRS, while HR remained unaltered with respect to baseline values before induction of anesthesia. The bivariate model suggests that the feedback pathway of cardiac baroreflex could be blunted by propofol induced anesthesia and that the feedforward pathway could be unaffected by anesthesia.

Heart rate variability (HRV) and respiration recorded during sleep from 8 patients suffering from difficult-to-control asthma were studied to investigate autonomic nervous system control of cardiac and respiratory activities, and of cardio-respiratory coupling during different sleep stages.

Biomedical signals constitute the “epiphany”, i.e the external manifestation of the functioning of the involved physiological systems. Many of these systems like cardiovascular or central nervous systems are indeed complex and that is confirmed by both the structure of physiological modelling which is generally employed to explain various pathophysiological phenomena and by the richness of information detectable from the signals, in most of the times with strong linear and nonlinear interactions with other biological systems. In particular, this behaviour may be accordingly described by means of what was called MMMM-paradigm (i.e. multivariate, multiorgan, multimodal and multiscale). Such an approach to physiological studies emphasizes where the genesis of their complexity is potentially allocated and how it is possible to detect information from it, through a proper processing of the available data. It is believed that this paradigm will contribute to a more olistic vision of pathophysiology, by considering patient as a whole not simply as the sum of his/her constituent parts. Medical diagnosis a well as a modern concept of preventive medicine which has to take care also of healthy population could largely profit by this methodological and technological approach.

Monitoring procedures are the basis to evaluate the clinical state of patients and to assess changes in their status, thus providing necessary interventions in time. To obtain this important objective it is necessary to integrate technological development with systems performing biomedical knowledge extraction and classification. Methods extracting non linear characteristics from HRV signal are presented and discussed to stress that integrated and multiparametric signal processing approaches can contribute to new diagnostic and classification indices. Examples report heart rate variability analysis in long periods in patients with cardiovascular disease. Fetal ECG monitoring is another example. In this case, coupling nonlinear parameters and linear time and frequency techniques increases diagnostic power and reliability of the monitoring. The paper shows that integrated signal analysis is very helpful to describe pathophysiological mechanisms involved in the cardiovascular and neural system control. It is a reliable basis to set up knowledge-based monitoring systems.

The Amplitude of Dominant T-Wave Alternans (ADTWA) is a recently introduced index which quantifies the presence of microvolt T Wave Alternans (TWA) on surface ECG recordings. In this paper we investigate the reliability of ADTWA and its robustness against broadband noise. At this regard, we generated synthetic 12-leads ECG recordings through a forward electrophysiological model and we added TWA, at different extent, by modulating the variation of the repolarization times of transmembrane action potentials across even and odd beats. Using a stochastic model, we derived an analytical relationship between the repolarization variation injected into the model and TWA at the surface, thus offering a strategy to evaluate lead sensitivity. In terms of robustness, the results of the simulations show that ADTWA correctly measured the amplitude of synthetic TWA with an average error of 3.3% ± 5.8% in absence of noise. When a 100 μV peak-to-peak broadband noise is present, its effects on estimation errors were kept limited by singular value decomposition on which ADTWA builds.

Energy expenditure (EE) is a parameter of great relevance in studies involving the assessment of physical activity. However, most reliable techniques for EE estimation are impractical for use in free-living environments, and those which are practically useful often poorly track EE when the subject is working to change their altitude, for example when ascending or descending stairs or slopes. The aim of this study is to evaluate the utility of adding barometric pressure related features, as a surrogate measure for altitude, to existing accelerometry related features to estimate the subject's EE. The EE estimation system described is based on a triaxial accelerometer (triax) and a barometric pressure sensor. The device is wireless, with Bluetooth connectivity for data retrieval, and is mounted at the subject's waist. Using a number of features extracted from the triax and barometric pressure signals, a linear model is trained for EE estimation. This EE estimation model is compared to its counterpart, which solely utilizes accelerometry signals. A protocol (comprising lying, sitting, standing, walking phases) was performed by 13 healthy volunteers (8 male and 5 female; age: 23.8 ± 3.7 years; weight: 70.5 ± 14.9 kg), whose instantaneous oxygen uptake was measured by means of an indirect calorimetry system. The model incorporating barometric pressure information estimated the oxygen uptake with the lowest mean square error of 4.5 ± 1.7 (mlO(2).min(-1).kg(-1))(2), in comparison to 7.1 ± 2.3 (mlO(2).min(-1).kg(-1))(2) using only accelerometry-based features.

Falls-related injuries in the elderly population represent one of the most significant contributors to rising health care expense in developed countries. In recent years, falls detection technologies have become more common. However, very few have adopted a preferable falls prevention strategy through unsupervised monitoring in the free-living environment. The basis of the monitoring described herein was a self-administered directed-routine (DR) comprising three separate tests measured by way of a waist-mounted triaxial accelerometer. Using features extracted from the manually segmented signals, a reasonable estimate of falls risk can be achieved. We describe here a series of algorithms for automatically segmenting these recordings, enabling the use of the DR assessment in the unsupervised and home environments. The accelerometry signals, from 68 subjects performing the DR, were manually annotated by an observer. Using the proposed signal segmentation routines, an good agreement was observed between the manually annotated markers and the automatically estimated values. However, a decrease in the correlation with falls risk to 0.73 was observed using the automatic segmentation, compared to 0.81 when using markers manually placed by an observer.

This work aims at studying the autonomic nervous system (ANS) response to hemodialysis (HD) treatment in a population of end stage renal disease (ESRD) patients. ECG Holter recordings and whole body bioimpedance spectroscopy measurements were performed for each patient. Patients were classified according to the fluid overload (FO) values and the systolic blood pressure (SBP) measured before HD. Time domain and frequency domain indices from heart rate variability (HRV) signals were measured for the first 30 minutes and last 30 minutes of HD, the first hour after HD, and night (12.00 p.m.–4 a.m.). Significant differences were obtained in fluid overloaded but normotensive patients (Group IV) with respect to fluid overloaded and hypertensive patients (Group I) and normohydrated and normotensive patients (Group N+Dx). In particular, SDNN, RMSSD, SDSD, pNN50%, indices resulted significantly higher in Group IV with respect to the other groups. Overhydrated patients with hypertension (Group I) showed a blunted parasympathetic activity, which is supposed to contribute to hypertension.

We present a preliminary quantitative study aimed at developing an optimal standard protocol for automatic classification of specific affective states as related to human- computer interactions. This goal is mainly achieved by comparing standard psychological test-reports to quantitative measures derived from simultaneous non-invasive acquisition of psychophysiological signals of interest, namely respiration, galvanic skin response, blood volume pulse, electrocardiogram and electroencephalogram. Forty-three healthy students were exposed to computer-mediated stimuli, while wearable non-invasive sensors were applied in order to collect the physiological data. The stimuli were designed to elicit three distinct affective states: relaxation, engagement and stress. In this work we report how our quantitative analysis has helped in redefining important aspects of the protocol, and we show preliminary findings related to the specific psychophysiological patterns correlating with the three target affective states. Results further suggest that some of the quantitative measures might be useful in characterizing specific affective states.

A falls detection system, employing a Bluetooth-based wearable device, containing a triaxial accelerometer and a barometric pressure sensor, is described. The aim of this study is to evaluate the use of barometric pressure measurement, as a surrogate measure of altitude, to augment previously reported accelerometry-based falls detection algorithms. The accelerometry and barometric pressure signals obtained from the waist-mounted device are analyzed by a signal processing and classification algorithm to discriminate falls from activities of daily living. This falls detection algorithm has been compared to two existing algorithms which utilize accelerometry signals alone. A set of laboratory-based simulated falls, along with other tasks associated with activities of daily living (16 tests) were performed by 15 healthy volunteers (9 male and 6 female; age: 23.7 +/- 2.9 years; height: 1.74 +/- 0.11 m). The algorithm incorporating pressure information detected falls with the highest sensitivity (97.8%) and the highest specificity (96.7%).

Since the late 90s both single and multi-electrode neuronal network recording signals have been characterized as endowed with long-term memory, i.e. a long-lasting decreasing correlation in the signal second-order statistics (e.g., autocorrelation function). Such a characteristic, typical of many fractal processes, indicate that the signal actual value strongly depends on its “past history”. At the same time, neuronal networks have been modeled as Self-Organized Criticality (SOC) systems, i.e., systems that independently organize themselves in a critical state. In this paper we analyze the estimations of long-term memory behavior for in-vitro and in-vivo neuronal networks (both by using original and literature data) and discuss such a results by the light of the SOC modeling.

In this work, bispectral and cross-bispectral analysis was applied to LFP and electromyographic (EMG) signals recorded in a patient with essential tremor, implanted in the subthalamic nucleus (STN) with DBS electrodes. Because the cross-bispectrum has sometimes a not plain interpretation, simulations were conducted to better understand the reliability of the estimation and to obtain a more detailed result interpretation. Both STN LFPs and the EMG had a predominant and highly coherent peak at 5Hz and a smaller peak at 10Hz. In addition, a high-beta activity (25–35 Hz) was detected in the STN. Whereas bispectral analysis on STN LFPs disclosed that STN rhythms are independent, cross-bispectral analysis disclosed a significant non-linear synchronizations between LFPs and EMG, suggesting that the 5Hz subthalamic rhythm and the 5Hz muscular rhythm were non-linearly interacting and that this interaction produced at least part of the 10Hz oscillation in the EMG. The results of this work suggest that non-linear synchronizations between subcortical structures and peripheral muscles were found to have a role in the pathophysiology of tremor.

Proteomic analysis may be useful to investigate disorders of the central nervous system, in order to explore the protein content of cells and of biological fluids in respect of the onset and evolution of diseases. Today, one of the most used proteomic approach includes the separation and visualization of proteins by means of two-dimensional gel electrophoresis (2DE). However the development of fully automatic strategies in extracting information from gel images is still a challenging task. In this paper we applied a computational strategy to the aim of obtaining a compact representation of the original data. This method was applied to an experimental protocol including two different clinical groups of amyotrophic lateral sclerosis (ALS) and peripheral neuropathy patients : 32 2DE maps generated from cerebrospinal fluid (24 pathologic and 8 control subjects) were processed. Quantitative features were extracted to describe each image and dealt with the dimension reduction technique of local tangent space alignment (LTSA). The discovered low-dimensional structure allows to see the gel of the dataset as separable, according to their clinical conditions, showing the informativeness of the adopted descriptors and providing the bases for classification of this kind of samples.

In this paper, we present a home device for the continuous monitoring of sleep and investigate its reliability regarding sleep evaluation. The system has been particularly designed for healthy people and for preventive purposes. It is not obtrusive and therefore can be used every night without impeding sleep in itself and without interfering with the normal way of life. The signal used for sleep evaluation is the HRV derived from the ECG recorded by means of a sheet and a pillow. Patients in a sleep lab and healthy subjects at home were monitored during sleep with the textile system, while also standard ECG and respiration were recorded. For the textile ECG sensor, coverage of the signal on a beat-to-beat basis ranged from 47,9 - 95,8% of the overall night for the healthy subjects, with a mean coverage of 81,8%. In the group of sleep laboratory patients, the mean coverage was lower - 64,4% - although even in this group the coverage of a single night ranged up to 98.4%. After frequency analysis, the spectral parameters used for sleep staging and derived at the same time from standard and textile ECG signals were compared. The trends along the night are very similar, indicating the possibility of using textile HRV for sleep evaluation.

In neurobiology an even bigger role is covered by acquisition and processing of data obtained from micro-electrode array (MEA) technology, where a fixed number of channels record in vitro neural spiking activity. In this work we developed a software-tool implementing a specific method to analyze time data series of this spiking activity. Our aim is to quantify a global measure of correlation and dependence between activities of neurons belonging to different MEA channels. Methods include cross-correlation, mutual information and dynamic time warping. These techniques can extract specific information about behavior of the neurons matrices. In order to synthesize a global measure of correlation in spiking neuron activity, we applied a sub-optimal criterion based on genetic algorithms (GA): GA help us to sort MEA channels with most similar and coordinated activity, and finally to estimate our index of global similarity. Thanks to this index we tried to test the existence of a sort of self-coordinating neuronal network, evolving in time and regulating the ensemble neural behavior under specific external stimuli, such as the chemical ones here studied

The human brain substantially differs between males and females. A recent study demonstrated the existence of significant gender-related differences in the electrical activity (local field potentials, LFPs) recorded from the human subthalamic area in Parkinson's disease. However, these results do not clarify whether these differences are reflected also in the neural network architecture and in the information processing at the basal ganglia level. In this work, we sought for possible gender-related differences in non-linear phase synchronizations between LFP rhythms by means of bispectral analysis. Bispectral analysis of LFP disclosed significant gender-related differences in the non-linear phase synchronizations between rhythms. Our results showed that, besides the already known differences in spectral power of rhythms expressed in the STN area in PD, also the non-linear phase synchronizations between those rhythms differ across genders. In particular, in the absence of levodopa stimulation, males had higher synchronization within the low-frequency band, whereas the pattern of synchronizations between and within the other rhythms was similar. After dopaminergic therapy had restored more physiological dopamine levels, the non-linear phase coupling phenomena were similar between the two genders. Gender-related differences in the phenomenology of PD as well as in LFPs cannot be explained only in terms of modulation of rhythm amplitude. In conclusion, sexual-dimorphism in the central nervous system involves the human STN, not only in terms of amplitude of the oscillatory, but also in terms of nonlinear phase synchronizations among rhythms

The study of cognitive functions is a major challenge of the modern functional imaging. Activation of specific cerebral area is obtained from the observation of physic characteristic affected by changes occurring in the blood flow resulting from an increased metabolic consumption. In this work two imaging techniques are used, the functional magnetic resonance (fMRI) and the near infrared spectroscopy (NIRS), in order to assess cerebral performance during the execution of a well known sustained attention task, the Conners' continuous performance test (CPT). With fMRI analysis were found activations in the frontal, parietal and supplementary motor areas, whereas NIRS system showed a region-wise difference in the variations of parameters and different activation trend localized in the middle-right frontal area. The combined analysis of the two techniques allows to obtain more detailed information and places itself as a first step toward a result of multimodal image integration.

In this paper we discuss the possibility of performing a sleep evaluation form the heart rate variability (HRV) and respiratory signals. This is particularly useful for wearable devices. The HRV and the respiration were analyzed in the frequency domain and the statistics on the spectral and cross-spectral parameters put into evidence the possibility of a sleep evaluation on their basis. Additional information can be achieved from the number of microarousals recognized in correspondence of typical modifications in the HRV signal.

The present paper describes procedures for the evaluation of sleep quality and for stress management based on the analysis of the HRV and respiration signals. The analysis is though for a device aimed to provide support to people who want to develop a healthier lifestyle, with major focus on cardiovascular disease prevention.

We present results obtained from the analysis of 50 heart rate variability series (HRV) which have been extracted from Holter recordings in the 24-hours in normal subjects and pathological patients. Data have been collected inside a multicentric research program, which aimed at the nonlinear analysis of HRV series. Multifractal approaches such as generalized structure functions have been used to characterize the HRV signal. Moreover, classical parameters for the analysis of the HRV signal over long time scales have been considered to perform a proper comparison. We considered classical time-domain indexes, "monofractal" characteristics (1/f/sup /spl alpha// spectrum; detrended fluctuation analysis) and a regularity statistic (approximate entropy). The hypothesis of nonlinearity for the HRV signal has been verified by computing the generalized structure function on a set of surrogate data (amplitude adjusted surrogate data). In most cases, the multifractal spectrum of the original HRV series significantly differs (t-test), from those obtained from surrogate signals. This result can be associated with the presence of nonlinear correlations in the HRV signal. Moreover, results show that nonlinear parameters can be used to separate normal subjects from patients suffering from cardiovascular diseases.

The applicability of OSEM reconstruction algorithms with space dependent resolution recovery to clinical FDG-PET studies is verified. The performance of the 2D algorithm is improved by means of a low resolution initialization and by a infra-iteration Metz filtering. Effects of different rebinning algorithms on 3D data are assessed, concluding that they do not alter the transaxial plane blurring parameters, thus permitting a straightforward application of 2D OSEM reconstruction after rebinning, with the same system matrix. Finally axial degradation was also quantified, finding that FORE is the best rebinning method to be combined with the 2D OSEM reconstruction.

Obstructive sleep apnea (OSA) is a common sleep disorder which causes the reduction of the upper airways muscular tone collapsing the same. This disorder is associated with bad sleep quality leading to excessive daytime sleepiness and increased risk of cardiovascular diseases. The main therapies are the continuous positive airways pressure (CPAP) and auto-CPAP which are devices that force a positive pressure in the upper airways in different manner. The aim of this work was to evaluate the different response of the cardiovascular autonomic nervous system to the therapies. We applied mono and bivariate autoregressive time invariant spectral estimation methods to the heart rate variability and respiratory signals coming from polisomnography executed on 12 patients: 4 during APAP, 4 during CPAP and 4 in basal condition as group of control. We principally found significant differences between groups in HRV variance, low-to-high frequencies ratio (LF/HF) and different coherence levels between signals. These preliminary results seem to suggest different effects of the therapies, with a greater sympathetic activation for APAP group and a better efficacy of CPAP in bringing back pathological condition closer to normality

The genesis of seizures in idiopatic generalized epilepsy (IGE) is investigated in a population of 10 patients (5 affected by Juvenile Absence Epilepsy, or JAE, and 5 affected by Juvenile Myoclonic Epilepsy, or JME). The aim of the study is the identification of a talamic or cortical origin of the paroxistic activity. The mutual synchronization among EEG channels was evaluated trough the cross conditional corrected entropy (CCCE) calculated on the row signals and after the application of the Laplacian operator. In JAE patients a leading role of the left frontal lobe was put into evidence in correspondence of the seizure, while in JME patients a leading electrode was not identified, suggesting a possible talamic origin of the seizure.

Analysis of variability series obtained from synchronous recordings of ECG and arterial blood pressure (ABP) signals has been applied to 9 patients during haemodialitic treatment. Information about the activity of neural systems controlling the heart has been extracted through autoregressive spectral analysis and estimation of time domain parameters. Hypotension prone (unstable) patients show values of LF/HF ratio, which measures the sympathovagal balance, always lower than hypotension resistant (stables) ones. Results suggest an interpretative hypothesis for which the reduced response of the baroreceptive mechanism in unstable patients could be responsible for the hypotensive crises.

Signal averaged acoustic quantification (SAAQ) with time-warping (TW) preserves the morphology of SAAQ waveforms allowing reliable noninvasive quantification of amplitude- and timing-indexes of left ventricular (LV) function, which cannot be assessed with simple signal averaging. We tested the ability of this technique to track pharmacologically induced changes in LV function during residual viability study. LV volume signals obtained (Sonos 5500) in 10 patients with dilated cardiomyopathy under control conditions and during low-dose dobutamine infusion (10 mcg/kg/min) were digitized at 300 Hz. In each subject and for each experimental condition LV volume signals were averaged (16 consecutive beats) with the TW technique. Averaged waveforms and time-derivatives were used to identify the phases of LV filling and calculate end-systolic and end-diastolic volumes, stroke, peak rapid and peak atrial filling rates, peak ejection rate, duration of heart cycle, systole, rapid filling and atrial filling phases. The calculated indices reflected the low-dose dobutamine-induced changes in LV function, evidencing variations both in rapid filling and systolic phases. Signal averaging with TW allows reliable calculation of indices of LV systolic and diastolic function, and can be used to monitor their changes related to residual viability examinations.

The heart rate variability (HRV) signal was studied by means of autoregressive recursive spectral analysis in the first hours after myocardial infarction and during thrombolytic therapy. The HRV signal was obtained as the sequence of the RR time intervals; the frequency analysis was performed by means of an autoregressive time-variant model, and spectral parameters able to quantify the sympatho-vagal balance (LF and HF power and their ratio LF/HF) have been obtained on a beat-to-beat bases. All the selected patients had an infero-postero-lateral infarction, and different responses to the thrombolytic therapy were identified: reperfusion and no reperfusion. The behaviour of the spectral parameters differs in subject with reperfusion and with no reperfusion.

We propose new methods for the extraction of self-similarity characteristics from 24-hour Heart Rate Variability signals (HRV). The first method is deduced from the "Second Order Difference plot": the distribution dispersion obtained by a sequence HRV appears to be an estimation of the long-term correlation (H) of the signal. The second method (CYCLES) provides a decomposition of the self-similarity characteristics as a fraction of the time scales. We analyze 10 Normal, 10 Heart Failure (HF) and 2 transplanted subjects. Results show that, in normal subjects, the signal information is only present in very-short and long period (control scales), as the pathological group has different and heterogeneous patterns, with a global effect of reduced correlation in the control scales.

Cardiovascular autonomic rhythms (Low Frequency, LF and High Frequency, HF) can be detected in spontaneous Pupil Diameter (PD) fluctuations. Aim of this study is to investigate the origin of these pupil fluctuations in humans, using a non-invasive modulation of carotid baroreceptors by Neck Suction (NS). ECG, Respiration and NS pressure from 10 normal subjects were recorded simultaneously to PD fluctuations. The equipment for the PD measurement and the NS stimulation was developed in the authors' laboratory. To distinguish between the sympathetic and parasympathetic efferent activity the authors' studied the response of the pupil to the NS at two different frequencies of stimulation (0.10 and 0.20 Hz). Their findings showed that baroreceptor stimulation induces pupil fluctuations locked to both stimulation frequencies, suggesting blood pressure fluctuations propagate to the pupil via carotin baroreceptors afferent pathways. However a central contribution cannot be excluded.

Activation patterns underlying the electrical activity of the heart during Atrial Fibrillation (AF) are not entirely random. The aim of this study was to estimate the degree of coupling between two atrial sites by means of two indexes (Independence of Complexity and Independence of Predictability, IC and IF) based on a multivariate embedding procedure. We analyzed activation sequence of two atrial sites in 30 informed subjects during chronic AF of type I, II and III (Wells' classification), 10 episodes each type, lasting 6 seconds. Surrogates were used to reject the hypothesis that the time series were generated by linear stochastic dynamics. In type I and II AF, IC and IP were significantly lower than their surrogates. In type III no coupling was detected IC and IP succeeded in detecting a nonlinear interaction between different atrial sites in type I and to a minor extent, type II AF.

The contribution of vasomotion to low frequency (LF, 0.03-0.14 Hz) waves in arterial pressure (AP, finger pletismography) and RR variabilities is addressed by comparing the spectra of RR, systolic AP (SAP), diastolic AP (DAP), and peripheral resistances (RES, mean AP over mean flow by laser Doppler) in the palmar region. In 22 normal volunteers, recordings were performed at rest (R) in clinostatic position and during mild exercise (EXEl, 10% maximum effort) cycling in the same position. LF predominance over the high frequency (HF, respiratory frequency) component was higher in SAP and even more in DAP than in RR; HF was virtually absent in RES. The LF central frequency shifted from 0.100 Hz for RR to 0.091 for SAP, 0.085 for DAP, and 0.073 for RES. EXEl activated LF and inhibited HF in RR; but power and frequency relationships in vascular parameters were not significantly changed.

We addressed questions related to the cortical sources involved in the spatial short term memory in humans. Such sources were estimated from non-invasive EEG recordings by using linear inverse procedure in a group of 9 normal healthy subjects. Analyzed neural activations from EEG recordings were obtained in regions of interest (ROIs) of few squared cm at the cortical level. Structural equation modeling (SEM) technique was then used to assess effective connectivity between cortical ROIs. Results showed an increase of connectivity of cortical prefrontal and premotor areas during the task phase in which an image has to be held in the memory before an appropriate decision has to be made by the subjects.

An algorithm based on fast wavelet transform has been developed for detecting diastolic and systolic coronary blood flow velocity signals characteristic patterns. The fast wavelet transform is used as a denoising tool to distinguish coronary blood flow waves from major noise, artifacts and baseline drift. Start-end diastolic-systolic fiducial points have been automatically detected to measure coronary blood flow velocity characteristic parameters. Feasibilty of scientific and clinical investigation assessment of coronary artery disease is presented through phasic coronary blood flow beat-to-beat analysis.

Analysis of interference and phase locking phenomena between respiration and short-term Heart Rate and Blood Pressure variability signals have been performed by using nonlinear signal processing techniques, such as Poincare Maps, Recurrence Maps, Time-Space Separation Plots and Bispectrum Estimation. Data obtained from normal subjects confirm the hypothesis of non-linear interaction of Low and High frequency rhythms with respiration.

To evaluate how small decreases and increases of central volume influence heart rate and arterial pressure control multivariate autoregressive techniques were used to quantify the interactions between respiration, RR interval and arterial blood pressure during random interval breathing, at 3 low levels of lower body negative pressure and 3 low levels of increased central volume. In addition to the classic spectral parameters for each signal, the algorithms were used to derive the closed-loop feedforward and feedback gains for the baroreflex and the effects of respiration on RR interval. With reductions of central volume below control, baroreflex and respiratory sinus arrhythmia gains were generally reduced, while with increases of volume above control, they increased only for the first two levels, and decreased at the highest volume. These findings indicate that reflex heart rate control is maximum with mild hypervolemia, and are consistent with the presence of the Bainbridge reflex in healthy humans.

Since 1985, European Union (EU) countries have decided on a new procedure for legislation and regulations concerning industrial products (the so-called "new approach"). The basic philosophy is that mandatory laws (EU Directives) define only the essential requirements for safety and performance, in each class of products, which must be fulfilled in order to have free circulation inside the EU countries. Directives do not contain the specific technical requirements which are demanded by technical standards, but introduce the basic philosophy of general definitions, classes and relative modalities for certification, the tasks of manufacturers, political authorities, notified bodies and users, technology evaluation, market assessment, and so on. The important market segment of medical equipment and devices is regulated by means of three Directives, one of which is still at its final stage before official approval. An important integration of legislative (mandatory) requirements with technical (non-mandatory) requirements has been reached, in order to fulfil two objectives: (i) an easy exchange of products inside the EU countries, and (ii) maintaining a reasonably high level of safety and performance standards for this very delicate segment of the industrial market. In this paper, the basic characteristics of this regulation policy are reviewed, with the important implications of interacting and integrating with other regulations and standards in a global market dimension.

Recent approaches to the study of nonlinearity in biological systems have found a powerful tool in the approximate entropy (ApEn) estimation. ApEn is a family of statistic indices that measures different degrees of regularity in time series without any a priori hypothesis about the system structure generating them. In this paper we analyze its ability to distinguish, in the short period, different physiological conditions in which the cardiovascular control system can influence the heart rate variability signal (HRV). At the same time we show results confirming a significant separation in myocardial infarction populations on the basis of the ApEn index. The experimental work also discusses the choice of parameters for a correct ApEn index estimation and its link with the neural mechanisms controlling HRV signal.

The paper proposes a quantification of heart rate variability signal (HRV) by a multiparametric analysis. The goal is to verify a possible improvement of the diagnostic and prognostic power of the HRV analysis. Conventional indices in time and in frequency domain have been associated with newer parameters based on nonlinear dynamics, such as approximate entropy and /spl alpha/ slope of the 1/f spectrum. The study considers HRV signals derived from 24 hours Holter ECG recordings. The population consists of 17 congestive heart failure patients (CHF) and 5 normal subjects. Results show differences in heart rate control inside the CHF group and between normal and CHF patients. The nonlinear HRV indices may contribute prognostic value to complement traditional HRV measures.

The event related potentials recorded before, during and after the voluntary movement of a finger, contain relevant information regarding the nervous mechanism involved at central level. As the phenomena of interest take place in short time, the frequency information was obtained from the signal by means of a recursive implementation of a bivariate autoregressive (AR) model. The spectral and cross-spectral parameters have been calculated in correspondence of each sample in the signal and for each recording electrode in the 10-20 system. The time course of the power in different frequency bands was obtained together with coherence and phase relationships. The algorithm allowed the evaluation of the time instant in which the events of interest take place, evidencing the beginning and the end of event related desynchronization and event related synchronization giving a global insight into the observed phenomena.

The aim of this study was to use Recurrence Plot Analysis to test the hypothesis of deterministic non-linear dynamics of Atrial Period (AP) during type I Atrial Fibrillation (AF) (Wells' classification). The authors used three variables to quantify Recurrence Plots: Percent Recurrence (PR), Percent Determinism (PD) and Entropy of Recurrences (ER). They analyzed bipolar intra-atrial electrograms of electrically induced AF recorded in 3 different atrial sites in 15 informed subjects. The AP of each each site was obtained by using a peak detection algorithm based on threshold crossing. The authors compared the real data results with those obtained from their stochastic surrogate series (phase randomization). PR, PD and ER values obtained for the surrogate data were significantly lower than those values from original data, confirming the hypothesis of a nonlinear deterministic dynamics of the atrial depolarization period during AF.