Search

Heart failure (HF) is a complex syndrome which can be the exit of different etiologies which lead to a common clinical picture. Treatment outcome is modest and new insights into pathophysiology are needed to improve effectively diagnosis, prognosis, and therapy. As a complex disease, it can really benefit from the new approach provided by molecular medicine where information about the cell program can deeply change the patient management. The comprehension of molecular mechanisms relies on new technologies which are able to provide data at the different levels of the cell workflow. A proper processing of these data is as essential as the generation of data itself, together with an integrative approach able to exploit observations at different scales, starting from the molecular levels up to an effective assessment of phenotype with cardiovascular and systemic parameters. In this paper, evidences concerning current understanding of HF, passing from one scale to another, are reported, to underline how functional genomics and systems biology perspective, fostered by the combination of biotechnologies and bioinformatic methods, may transform the concepts of diagnosis and, consequently, of treatment, toward the fulfillment of the invoked patient-specific medicine.

The 11 papers in this special section focus on multiscale biomedical signal and image modeling and analysis. This issue is the third part dedicated to the topic.

This paper presents a method for obstructive sleep apnea (OSA) screening based on the electrocardiogram (ECG) recording during sleep. OSA is a common sleep disorder produced by repetitive occlusions in the upper airways and this phenomenon can usually be observed also in other peripheral systems such as the cardiovascular system. Then the extraction of ECG characteristics, such as the RR intervals and the area of the QRS complex, is useful to evaluate the sleep apnea in noninvasive way. In the presented analysis, 50 recordings coming from the apnea Physionet database were used; data were split into two sets, the training and the testing set, each of which was composed of 25 recordings. A bivariate time-varying autoregressive model (TVAM) was used to evaluate beat-by-beat power spectral densities for both the RR intervals and the QRS complex areas. Temporal and spectral features were changed on a minute-by-minute basis since apnea annotations where given with this resolution. The training set consisted of 4950 apneic and 7127 nonapneic minutes while the testing set had 4428 apneic and 7927 nonapneic minutes. The K-nearest neighbor (KNN) and neural networks (NN) supervised learning classifiers were employed to classify apnea and non apnea minutes. A sequential forward selection was used to select the best feature subset in a wrapper setting. With ten features the KNN algorithm reached an accuracy of 88%, sensitivity equal to 85%, and specificity up to 90%, while NN reached accuracy equal to 88%, sensitivity equal to 89% and specificity equal to 86%. In addition to the minute-by-minute classification, the results showed that the two classifiers are able to separate entirely (100%) the normal recordings from the apneic recordings. Finally, an additional database with eight recordings annotated as normal or apneic was used to test again the classifiers. Also in this new dataset, the results showed a complete separation between apneic and normal recordings.

Today, the concept of brain connectivity plays a central role in the neuroscience. While functional connectivity is defined as the temporal coherence between the activities of different brain areas, the effective connectivity is defined as the simplest brain circuit that would produce the same temporal relationship as observed experimentally between cortical sites. The most used method to estimate effective connectivity in neuroscience is the structural equation modeling (SEM), typically used on data related to the brain hemodynamic behavior. However, the use of hemodynamic measures limits the temporal resolution on which the brain process can be followed. The present research proposes the use of the SEM approach on the cortical waveforms estimated from the high-resolution EEG data, which exhibits a good spatial resolution and a higher temporal resolution than hemodynamic measures. We performed a simulation study, in which different main factors were systematically manipulated in the generation of test signals, and the errors in the estimated connectivity were evaluated by the analysis of variance (ANOVA). Such factors were the signal-to-noise ratio and the duration of the simulated cortical activity. Since SEM technique is based on the use of a model formulated on the basis of anatomical and physiological constraints, different experimental conditions were analyzed, in order to evaluate the effect of errors made in the a priori model formulation on its performances. The feasibility of the proposed approach has been shown in a human study using high-resolution EEG recordings related to finger tapping movements.

The aim of this paper is to quantify the coupling during phase-locking patterns by using the recurrence plot quantification approach. We found that the percent determinism of the recurrences and the entropy of recurrences--corrected for border effect induced by finite data lenght--succeeded in distinguishing three coupling conditions both in simulation signals and in real data from cardiorespiratory synchronization experiments.

A parametric method of identification of event-related (or evoked) potentials on a single-trial basis through an ARX (autoregressive with exogenous input) algorithm is discussed. The basic estimation of the information contained in the single trial is taken from an average carried out on a sufficient number of trials, while the noise sources, EEG and EOG, are characterized as exogenous inputs in the model. The simulations as well as the experimental results confirm the capability of the model of drastically improving the S/N (signal-to-noise) ratio in each single trial and satisfactorily identifying the contributions of signal and noise to the overall recording. A particularly efficient reduction of ocular artifacts is also achieved. >