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Monitoring the levels of sedation-analgesia may be helpful for managing patient stress on minimally invasive medical procedures. Monitors based on EEG analysis and designed to assess general anesthesia cannot distinguish reliably between a light and deep sedation. In this work, the Poincaré plot is used as a nonlinear technique applied to EEG signals in order to characterize the levels of sedation-analgesia, according to observed categorical responses that were evaluated by means of Ramsay Sedation Scale (RSS). To study the effect of high frequencies due to EMG activity, three different frequency ranges (FR1=0.5-110 Hz, FR2=0.5-30 Hz and FR3=30-110 Hz) were considered. Indexes from power spectral analysis and plasma concentration of propofol and remifentanil were also compared with the bispectral index BIS. An adaptive Neurofuzzy Inference System was applied to model the interaction of the best indexes with respect to RSS score for each analysis, and leave-one-out cross validation method was used. The ability of the indexes to describe the level of sedation-analgesia, according with the RSS score, was evaluated using the prediction probability (Pk). The results showed that the ratio SD1/SD2FR3 contains useful information about the sedation level, and SD1FR2 and SD2FR2 had the best performance classifying response to noxious stimuli. Models including parameters from Poincaré plot emerge as a good estimator of sedation-analgesia levels.

The level of sedation in patients undergoing medical procedures is affected by the interaction between the effect of the anesthetic and analgesic agents and the pain stimuli. The presence of the A118G single nucleotide polymorphism (SNP) in the OPRM1 gene affects the requirements of opioids for patients undergoing sedation-analgesia. The purpose of this work is to evaluate the influence of the SNP A118G in OPRM1 on EEG measures for the prediction of the response to pain stimulation during endoscopy procedure. The proposed measures were based on power spectral density and auto-mutual information function. It was found that the statistical performances of the EEG measures improved when the presence of the SNP was taken into account (prediction probability Pk>0.9).

The level of sedation in patients undergoing medical procedures evolves continuously, such as the effect of the anesthetic and analgesic agents is counteracted by pain stimuli. The monitors of depth of anesthesia, based on the analysis of the electroencephalogram (EEG), have been progressively introduced into the daily practice to provide additional information about the state of the patient. However, the quantification of analgesia still remains an open problem. The purpose of this work is to analyze the capability of prediction of nociceptive responses based on the time-frequency representation (TFR) of EEG signal. Functions of spectral entropy, instantaneous power and instantaneous frequency were calculated in order to predict the presence or absence of the nociceptive responses to different stimuli during sedation in endoscopy procedure. Values of prediction probability of Pk above 0.75 and percentages of sensitivity and specificity above 70% and 65% respectively were achieved combining TFR functions with bispectral index (BIS) and with concentrations of propofol (CeProp) and remifentanil (CeRemi).

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.

In the present document the multiscale entropy (MSE) methodology has been applied to analyze the complex behavior of the heart rate variability (HRV), in patients with aortic stenosis (AS). A set of healthy voluntaries have been used as a control group. MSE analysis calculates an entropy rate over different time scales to assess the complexity of time series, evaluating short-term and long-term correlations. Daytime and nighttime have been considered to study variations of the complexity inside the same group of population. A statistical analysis showed that entropy was significantly higher in healthy subjects than in AS subjects in all the scales during daytime, with exception at scale 1. During nighttime, entropy in healthy subjects was significantly higher than in AS subjects only in scales from 1 to 7. Multiscale entropy is helpful to characterize AS patients and distinguish them from healthy subjects.

Subjects with ischemic dilated cardiomiopathy tend to suffer episodes of sudden cardiac death, thus risk stratification is essential to establish an adequate therapy for the patients. In this work, a new methodology was proposed for the study of the heart rate variability by using a multiscale analysis based on the concept of entropy rates, for improving risk prediction in cardiac patients. Symbolic dynamics were applied to RR time series and sets of words in several scales were constructed. The multiscale regularity analysis was proposed by comparing the entropies, calculated using Shannon and Renyi definitions, of the series of words in different scales. The study considered the selection of the best parameters for the length of the words (l) and the order of the entropies (q). Statistical analysis with repeated measures and discriminant analysis revealed statistically significant differences (p-value<0.05) and a high percentage of well classified subjects in their different risk groups, with sensitivity, specificity and positive predictive values of 100%.