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espanolEl grafico de Poincare es un metodo no lineal que permite analizar la variabilidad de una senal, dibujando dicha senal contra ella misma retardada en el tiempo. Aunque este metodo ha sido aplicado al analisis del electroencefalograma (EEG) para la medicion de niveles de anestesia general, su aplicacion en la medicion de niveles de anestesia ligera aun no se ha abordado con profundidad. En este trabajo, se consideran varios tiempos de retardo en el grafico de Poincare para caracterizar el nivel de anestesia ligera en 110 pacientes sometidos a endoscopia digestiva. Los indices de Poincare son combinados con parametros espectrales de senales que contienen informacion del EEG y del EMG (electromiografia) para modelar los niveles de anestesia ligera, teniendo como referencia la escala de sedacion Ramsay. La mejor prediccion, utilizando indices del grafico de Poincare, del nivel de sedacion y analgesia (Pk = 0,711) se obtiene usando un retardo de 8 ms en el rango de frecuencia de 30-110 Hz y de 6 ms en el rango de frecuencia de 0,5 - 30 Hz, respectivamente. La combinacion de diferentes parametros permitio obtener valores de Pk = 0,799. EnglishPoincare plot is a non-linear method which allows analyzing the variability of a signal by plotting the signal against itself after a delay time. Although this method has been used to analyze the electroencephalogram (EEG) in order to assess general anesthesia, its application on light sedation has not been addressed in depth yet. In this work, several time-delays are considered in the Poincare plot to characterize levels of light anesthesia on 110 patients scheduled to digestive endoscopy. Poincare Plot indexes are mixed with parameters obtained from spectral analysis of signals with EEG and EMG (electromyography) information in order to model light anesthesia levels, according to Ramsay Sedation Scale. The best prediction, using indexes from Poincare plot, of sedation level and analgesia level (Pk = 0.711) is obtained with a time delay of 8ms in a frequency range from 30 Hz to 110 Hz and 6ms in a frequency range from 0.5 Hz to 30 Hz, respectively. Pk = 0.799 was obtained with a model mixing different parameters.

Background The aim of this observational study was to determine whether bilateral bispectral index (BIS) monitoring can detect seizures in epileptic patients. Methods Four-channel frontal BIS monitoring and standard 40-channel electroencephalography monitoring were conducted in epileptic patients undergoing evaluation for epilepsy surgery. The BIS numerical value, signal quality index, electromyography, suppression ratio, and color density spectral array were continuously recorded. In patients with electroencephalography-confirmed seizures, the mean value and trend (slope of linear regression) of bilateral BIS monitor parameters were analyzed from 1 minute before to 1 minute after seizure onset. Results Of 48 patients included in the study, 21 (43.8%) had at least 1 seizure. BIS numerical value was not able to detect focal or focal to bilateral tonic-clonic seizures. Considering all seizures, the only significant differences between recordings 1 minute before and 1 minute after seizure onset were a decrease in the signal quality index slope from 1 hemisphere (0.039±0.297 vs. -0.085±0.321, respectively; P=0.029) and in the mean signal quality index recorded from both hemispheres (left hemisphere: 65.775±30.599 vs. 61.032±26.285; P=0.016 and right hemisphere: 63.244±31.985 vs. 59.837±27.360; 0.029); these differences were not maintained after Hochberg adjustment for multiple comparisons. In seizures occurring during sleep, there was a change in the electromyography slope of 1 hemisphere before and after seizure onset (-0.141±0.176 vs. 0.162±0.140, respectively; P=0.038). There were variable responses in BIS parameters in the 3 patients who developed focal nonconvulsive seizure clusters. Conclusion Bilateral BIS monitoring was not able to detect the occurrence of seizures in epileptic patients.

It has been suggested that a viable strategy to improve complexity estimation based on the assessment of pattern similarity is to increase the pattern matching rate without enlarging the series length. We tested this hypothesis over short simulations of nonlinear deterministic and linear stochastic dynamics affected by various noise amounts. Several transformations featuring a different ability to increase the pattern matching rate were tested and compared to the usual strategy adopted in sample entropy (SampEn) computation. The approaches were applied to evaluate the complexity of short-term cardiac and vascular controls from the beat-to-beat variability of heart period (HP) and systolic arterial pressure (SAP) in 12 Parkinson disease patients and 12 age- and gender-matched healthy subjects at supine resting and during head-up tilt. Over simulations, the strategies estimated a larger complexity over nonlinear deterministic signals and a greater regularity over linear stochastic series or deterministic dynamics importantly contaminated by noise. Over short HP and SAP series the techniques did not produce any practical advantage, with an unvaried ability to discriminate groups and experimental conditions compared to the traditional SampEn. Procedures designed to artificially increase the number of matches are of no methodological and practical value when applied to assess complexity indexes.

The refined multiscale entropy (RMSE) approach is commonly applied to assess complexity as a function of the time scale. RMSE is normally based on the computation of sample entropy (SampEn) estimating complexity as conditional entropy. However, SampEn is dependent on the length and standard deviation of the data. Recently, fuzzy entropy (FuzEn) has been proposed, including several refinements, as an alternative to counteract these limitations. In this work, FuzEn, translated FuzEn (TFuzEn), translated-reflected FuzEn (TRFuzEn), inherent FuzEn (IFuzEn), and inherent translated FuzEn (ITFuzEn) were exploited as entropy-based measures in the computation of RMSE and their performance was compared to that of SampEn. FuzEn metrics were applied to synthetic time series of different lengths to evaluate the consistency of the different approaches. In addition, electroencephalograms of patients under sedation-analgesia procedure were analyzed based on the patient&rsquo, s response after the application of painful stimulation, such as nail bed compression or endoscopy tube insertion. Significant differences in FuzEn metrics were observed over simulations and real data as a function of the data length and the pain responses. Findings indicated that FuzEn, when exploited in RMSE applications, showed similar behavior to SampEn in long series, but its consistency was better than that of SampEn in short series both over simulations and real data. Conversely, its variants should be utilized with more caution, especially whether processes exhibit an important deterministic component and/or in nociception prediction at long scales.

Respiratory depression is a common adverse effect of propofol and remifentanil. We aimed to develop a model for respiratory depressant effects of propofol with remifentanil in patients undergoing endoscopy with sedation. Data were available for 136 patients undergoing endoscopy with sedation. Participants randomly received infusions of propofol and remifentanil. Predicted plasma concentrations, outputted by infusion pumps, were available. Transcutaneous arterial pressure of carbon dioxide (pCO2) was measured. Data were analyzed using nonlinear mixed-effects modeling methods. Covariate relationships were investigated for age, noxious stimuli (endoscopy tube insertion), and A118G genotype for the µ-opioid receptor (OPRM1). Participants had a median (range) age of 64.0 (25.0-88.0) years, weight of 70.0 (35.0-98.0) kg, and height of 164.0 (147.0-190.0) cm. Seven percent were recessive homozygous for OPRM1 polymorphism. An indirect-effect model with a "modulator" compartment best described pCO2 data (P < 0.001) over a direct-effect model. Remifentanil inhibited pCO2 removal with an IC50 of 1.13 ng/ml and first-order rate constant (ke 0) of 0.28 minute(-1). Propofol affected the modulator compartment with an IC50 of 4.97 µg/ml (no effect-site compartment). Propofol IC50 and remifentanil ke 0 were reduced with increasing age. Noxious stimuli and genotype were not significant covariates. An indirect-effect model with a rebound mechanism can describe remifentanil- and propofol-induced changes in pCO2 in patients undergoing noxious procedures. The model may be useful for identifying optimal dosing schedules for these drugs in a combination that provides adequate sedation but avoids respiratory depression.

The purpose of this study was to identify optimal target propofol and remifentanil concentrations to avoid a gag reflex in response to insertion of an upper gastrointestinal endoscope.Patients presenting for endoscopy received target-controlled infusions (TCI) of both propofol and remifentanil for sedation-analgesia. Patients were randomized to 4 groups of fixed target effect-site concentrations: remifentanil 1 ng•mL (REMI 1) or 2 ng•mL (REMI 2) and propofol 2 μg•mL (PROP 2) or 3 μg•mL (PROP 3). For each group, the other drug (propofol for the REMI groups and vice versa) was increased or decreased using the "up-down" method based on the presence or absence of a gag response in the previous patient. A modified isotonic regression method was used to estimate the median effective Ce,50 from the up-down method in each group. A concentration-effect (sigmoid Emax) model was built to estimate the corresponding Ce,90 for each group. These data were used to estimate propofol bolus doses and remifentanil infusion rates that would achieve effect-site concentrations between Ce,50 and Ce,90 when a TCI system is not available for use.One hundred twenty-four patients were analyzed. To achieve between a 50% and 90% probability of no gag response, propofol TCIs were between 2.40 and 4.23 μg•mL (that could be achieved with a bolus of 1 mg•kg) when remifentanil TCI was fixed at 1 ng•mL, and target propofol TCIs were between 2.15 and 2.88 μg•mL (that could be achieved with a bolus of 0.75 mg•kg) when remifentanil TCI was fixed at 2 ng•mL. Remifentanil ranges were 1.00 to 4.79 ng•mL and 0.72 to 3.19 ng•mL when propofol was fixed at 2 and 3 μg•mL, respectively.We identified a set of propofol and remifentanil TCIs that blocked the gag response to endoscope insertion in patients undergoing endoscopy. Propofol bolus doses and remifentanil infusion rates designed to achieve similar effect-site concentrations can be used to prevent gag response when TCI is not available.

© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works 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. _

In this work, a univariate and multivariate statistical analysis of indexes derived from heart rate variability (HRV) was conducted to stratify patients with ischemic dilated cardiomyopathy (IDC) in cardiac risk groups. Indexes conditional entropy, refined multiscale entropy (RMSE), detrended fluctuation analysis, time and frequency analysis, were applied to the RR interval series (beat-to-beat series), for single and multiscale complexity analysis of the HRV in IDC patients. Also, clinical parameters were considered. Two different end-points after a follow-up of three years were considered: (i) analysis A, with 151 survivor patients as a low risk group and 13 patients that suffered sudden cardiac death as a high risk group; (ii) analysis B, with 192 survivor patients as a low risk group and 30 patients that suffered cardiac mortality as a high risk group. A univariate and multivariate linear discriminant analysis was used as a statistical technique for classifying patients in risk groups. Sensitivity (Sen) and specificity (Spe) were calculated as diagnostic criteria in order to evaluate the performance of the indexes and their linear combinations. Sen and Spe values of 80.0% and 72.9%, respectively, were obtained during daytime by combining one clinical parameter and one index from RMSE, and during nighttime Sen = 80% and Spe = 73.4% were attained by combining one clinical factor and two indexes from RMSE. In particular, relatively long time scales were more relevant for classifying patients into risk groups during nighttime, while during daytime shorter scales performed better. The results suggest that the left atrial size, indexed to body surface and RMSE indexes are those that allow enhanced classification of ischemic patients in their respective risk groups, confirming that a single measurement is not enough to fully characterize ischemic risk patients and the clinical relevance of HRV complexity measures.

Nowadays, wavelet transform (WT) is widely used in the realm of signal denoising, has proven a high effectiveness in terms of time and quality concerning denoising methods. Despite there are several achievements denoising through wavelet thresholding methods, these do not disclose an optimal configuration. In this paper, we proposed a comparative performance analysis of several thresholding methods using WT; biological signals are denoised to obtain performance metrics. The efficiency of particular thresholding methods: rigrsure, sqtwolog, heursure and minimaxi using hard and soft thresholding are compared in the presence of low Gaussian noise also the effect of wavelet decomposition levels is analyzed. For wavelet decomposition, Haar wavelet is used. Experimental results show that by increasing decomposition levels likewise there was a denoising improvement in terms of root mean square error (RMSE) and correlation coefficient, however, from the fifth decomposition level RMSE and correlation coefficient slowly tends to get worse, also the threshold method rigrsure on soft thresholding improved RMSE of 1.77 to 1.03 and correlation coefficient of 99.32% to 99.71% while others techniques on both, soft and hard thresholding did not improve more than 1.1 in RMSE and 99.67% in correlation coefficient.

The analysis of heart rate variability (HRV) by nonlinear methods has been gaining increasing interest due to their ability to quantify the complexity of cardiovascular regulation. In this study, multiscale entropy (MSE) and refined MSE (RMSE) were applied to track the complexity of HRV as a function of time scale in three pathological conscious animal models: rats with heart failure (HF), spontaneously hypertensive rats (SHR), and rats with sinoaortic denervation (SAD). Results showed that HF did not change HRV complexity, although there was a tendency to decrease the entropy in HF animals. On the other hand, SHR group was characterized by reduced complexity at long time scales, whereas SAD animals exhibited a smaller short- and long-term irregularity. We propose that short time scales (1 to 4), accounting for fast oscillations, are more related to vagal and respiratory control, whereas long time scales (5 to 20), accounting for slow oscillations, are more related to sympathetic control. The increased sympathetic modulation is probably the main reason for the lower entropy observed at high scales for both SHR and SAD groups, acting as a negative factor for the cardiovascular complexity. This study highlights the contribution of the multiscale complexity analysis of HRV for understanding the physiological mechanisms involved in cardiovascular regulation.

The level of sedation in patients undergoing medical procedures is decided to assure unconsciousness and prevent pain. 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 was to analyze the capability of prediction of nociceptive responses based on refined multiscale entropy (RMSE) and auto mutual information function (AMIF) applied to EEG signals recorded in 378 patients scheduled to undergo ultrasonographic endoscopy under sedation-analgesia. Two observed categorical responses after the application of painful stimulation were analyzed: the evaluation of the Ramsay Sedation Scale (RSS) after nail bed compression and the presence of gag reflex (GAG) during endoscopy tube insertion. In addition, bispectrum (BIS), heart rate (HR), predicted concentrations of propofol (CeProp) and remifentanil (CeRemi) were annotated with a resolution of 1 s. Results showed that functions based on RMSE, AMIF, HR and CeRemi permitted predicting different stimulation responses during sedation better than BIS.

The level of sedation in patients undergoing medical procedures evolves continuously, affected by the interaction between the effect of the anesthetic and analgesic agents and the 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 improve the prediction of nociceptive responses with linear and non-linear measures calculated from EEG signal filtered in frequency bands higher than the traditional bands. Power spectral density and auto-mutual information function was applied in order to predict the presence or absence of the nociceptive responses to different stimuli during sedation in endoscopy procedure. The proposed measures exhibit better performances than the bispectral index (BIS). Values of prediction probability of P-k above 0.75 and percentages of sensitivity and specificity above 70% were achieved combining EEG measures from the traditional frequency bands and higher frequency bands.

The aim of the present study was to investigate the suitability of the Phase-Rectified Signal Averaging (PRSA) method for improved risk prediction in cardiac patients. Moreover, this technique, which separately evaluates acceleration and deceleration processes of cardiac rhythm, allows the effect of sympathetic and vagal modulations of beat-to-beat intervals to be characterized. Holter recordings of idiopathic dilated cardiomyopathy (IDC) patients were analyzed: high-risk (HR), who suffered sudden cardiac death (SCD) during the follow-up; and low-risk (LR), without any kind of cardiac-related death. Moreover, a control group of healthy subjects was analyzed. PRSA indexes were analyzed, for different time scales T and wavelet scales s, from RR series of 24 h-ECG recordings, awake periods and sleep periods. Also, the behavior of these indexes from simulated data was analyzed and compared with real data results. Outcomes demonstrated the PRSA capacity to significantly discriminate healthy subjects from IDC patients and HR from LR patients on a higher level than traditional temporal and spectral measures. The behavior of PRSA indexes agrees with experimental evidences related to cardiac autonomic modulations. Also, these parameters reflect more regularity of the autonomic nervous system CANS) in HR patients. (C) 2015 IPEM. Published by Elsevier Ltd. All rights reserved.

Myocardial ischaemia is hypothesized to stimulate the cardiac sympathetic excitatory afferents and, therefore, the spontaneous changes of heart period (approximated as the RR interval), and the QT interval in ischaemic dilated cardiomyopathy (IDC) patients might reflect this sympathetic activation. Symbolic analysis is a nonlinear and powerful tool for the extraction and classification of patterns in time-series analysis, which implies a transformation of the original series into symbols and the construction of patterns with the symbols. The aim of this work was to investigate whether symbolic transformations of RR and QT cardiac series can provide a better separation between IDC patients and healthy control (HC) subjects compared with traditional linear measures. The variability of these cardiac series was studied during daytime and night-time periods and also during the complete 24 h recording over windows of short data sequences of approximately 5 min. The IDC group was characterized by an increase in the occurrence rate of patterns without variations (0 V%) and a reduction in the occurrence rate of patterns with one variation (1 V%) and two variations (2 V%). Concerning the RR variability during the daytime, the highest number of patterns had 0 V%, whereas the rates of 1 V% and 2 V% were lower. During the night, 1 V% and 2 V% increased at the expense of diminishing 0 V%. Patterns with and without variations between consecutive symbols were able to increase the separation between the IDC and HC groups, allowing accuracies higher than 80%. With regard to entropy measures, an increase in RR regularity was associated with cardiac disease described by accuracy >70% in the RR series and by accuracy >60% in the QTc series. These results could be associated with an increase in the sympathetic tone in IDC patients.

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 contin uously since the effect of the anesthetic and analgesic agents is counteracted by noxious 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 was to analyze the capability of prediction of nociceptive responses based on refined multiscale entropy (RMSE). Functions based on RMSE and CeRemi permitted to predict different stimulation responses during sedation with better prediction probability than bispectral index.

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 was to analyze the capability of prediction of nociceptive responses based on the auto-mutual information function (AMIF). AMIF measures were calculated on EEG signal in order to predict the presence or absence of the nociceptive responses to endoscopy tube insertion during sedation in endoscopy procedure. Values of prediction probability of Pk above 0.80 and percentages of sensitivity and specificity above 70% and 70% respectively were achieved combining AMIF with power spectral density and concentrations of remifentanil.

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 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).

nesthesiology is a medical specialty in which uantification—the capacity to measure—is of major mportance. The basic principle of our interventions as nesthesiologists is based on the administration of drugs, he observation of the effect that these drugs have on the atient, and the adjustment of drug delivery depending on he response that is observed. The magnitude of this response epends on: 1. Factors directly related to the patient such as heir age, physical state, and the medication he/she is taking nd, 2. Contextual factors such as the type of surgical proceure, its urgency, the point of time within the procedure, and he complications that can appear (bleeding, hypothermia, tate of shock) to cite just a few of the most relevant aspects. bserving, adjusting, and magnitude—they are concepts that mply the possibility of measuring to precisely evaluate how he patient reacts in each and every moment of anesthetic rug delivery process. Technological developments over the last 30 or 40 years nd digitalization at all levels have favored the creation of ore exact and portable measuring systems. These systems

There is agreement among the scholars of democracy, from Rousseau and J. S. Mill to Tarrow and Putnam, that mass participation is essential for the life of a representative democracy. Political participation is the critical link between the citizenry and the governing process and “provides the mechanism by which citizens can communicate information about their interests, preferences, and needs and generate pressure to respond” (Verba, Schlozman, & Brady, 1995, p. 1). Keywords: political mobilization; political protest; conventional participation; framing processes; social constructionism; identity formation; cost-benefit analysis; resource mobilization theory; new social movements; grievances; injustice frame; identity frame; urgency frame

BACKGROUND: The increasing demand for anesthetic procedures in the gastrointestinal endoscopy area has not been followed by a similar increase in the methods to provide and control sedation and analgesia for these patients. In this study, we evaluated different combinations of propofol and remifentanil, administered through a target-controlled infusion system, to estimate the optimal concentrations as well as the best way to control the sedative effects induced by the combinations of drugs in patients undergoing ultrasonographic endoscopy. METHODS: One hundred twenty patients undergoing ultrasonographic endoscopy were randomized to receive, by means of a target-controlled infusion system, a fixed effect-site concentration of either propofol or remifentanil of 8 different possible concentrations, allowing adjustment of the concentrations of the other drug. Predicted effect-site propofol (C(e)pro) and remifentanil (C(e)remi) concentrations, parameters derived from auditory evoked potential, autoregressive auditory evoked potential index (AAI/2) and electroencephalogram (bispectral index [BIS] and index of consciousness [IoC]) signals, as well as categorical scores of sedation (Ramsay Sedation Scale [RSS] score) in the presence or absence of nociceptive stimulation, were collected, recorded, and analyzed using an Adaptive Neuro Fuzzy Inference System. The models described for the relationship between C(e)pro and C(e)remi versus AAI/2, BIS, and IoC were diagnosed for inaccuracy using median absolute performance error (MDAPE) and median root mean squared error (MDRMSE), and for bias using median performance error (MDPE). The models were validated in a prospective group of 68 new patients receiving different combinations of propofol and remifentanil. The predictive ability (P(k)) of AAI/2, BIS, and IoC with respect to the sedation level, RSS score, was also explored. RESULTS: Data from 110 patients were analyzed in the training group. The resulting estimated models had an MDAPE of 32.87, 12.89, and 8.77; an MDRMSE of 17.01, 12.81, and 9.40; and an MDPE of -1.86, 3.97, and 2.21 for AAI/2, BIS, and IoC, respectively, in the absence of stimulation and similar values under stimulation. P(k) values were 0.82, 0.81, and 0.85 for AAI/2, BIS, and IoC, respectively. The model predicted the prospective validation data with an MDAPE of 34.81, 14.78, and 10.25; an MDRMSE of 16.81, 15.91, and 11.81; an MDPE of -8.37, 5.65, and -1.43; and P(k) values of 0.81, 0.8, and 0.8 for AAI/2, BIS, and IoC, respectively. CONCLUSION: A model relating C(e)pro and C(e)remi to AAI/2, BIS, and IoC has been developed and prospectively validated. Based on these models, the (C(e)pro, C(e)remi) concentration pairs that provide an RSS score of 4 range from (1.8 μg·mL(-1), 1.5 ng·mL(-1)) to (2.7 μg·mL(-1), 0 ng·mL(-1)). These concentrations are associated with AAI/2 values of 25 to 30, BIS of 71 to 75, and IoC of 72 to 76. The presence of noxious stimulation increases the requirements of C(e)pro and C(e)remi to achieve the same degree of sedative effects.

This work has proposed a methodology based on the concept of entropy rates to study the complexity of the short-term heart-rate variability (HRV) for improving risk stratification to predict sudden cardiac death (SCD) of patients with established ischemic-dilated cardiomyopathy (IDC). The short-term HRV was analyzed during daytime and nighttime by means of RR series. An entropy rate was calculated on the RR series, previously transformed to symbol sequences by means of an alphabet. A statistical analysis permitted to stratify high- and low-risk patients of suffering SCD, with a specificity (SP) of 95% and sensitivity (SE) of 83.3%. To get a better characterization of short-term HRV, the study has also considered the adjustment of the parameters involved in the proposed methodology. Finally, a statistical analysis was applied to recognize valid prognostic markers.

Multiscale entropy (MSE) was proposed to characterize complexity as a function of the time-scale factor tau. Despite its broad use, this technique suffers from two limitations: (1) the artificial MSE reduction due to the coarse graining procedure and (2) the introduction of spurious MSE oscillations due to the suboptimal procedure for the elimination of the fast temporal scales. We propose a refined MSE (RMSE), and we apply it to simulations and to 24-h Holter recordings of heart rate variability (HRV) obtained from healthy and aortic stenosis (AS) groups. The study showed that the refinement relevant to the elimination of the fast temporal scales was more helpful at short scales (spanning the range of short-term HRV oscillations), while that relevant to the procedure of coarse graining was more useful at large scales. In healthy subjects, during daytime, RMSE was smaller at short scales (i.e., tau =1-2) and larger at longer scales (i.e., tau =4-20) than during nighttime. In AS population, RMSE was smaller during daytime both at short and long time scales (i.e., tau = 1 -11) than during nighttime. RMSE was larger in healthy group than in AS population during both daytime (i.e., tau = 2 -9) and nighttime (i.e., tau = 2). RMSE overcomes two limitations of MSE and confirms the complementary information that can be derived by observing complexity as a function of the temporal scale.

The purpose of this study was to provide a methodology in order to characterize autonomous nervous system (ANS) changes before, during and after percutaneous transluminal coronary angioplasty (PTCA). This methodology, based on a multiscale information analysis (MIA) of the heart rate variability, has taken into account entropy rates. The study group consisted of 66 patients undergoing PTCA: 1 in the left main, 21 in the left anterior descending, 30 in the right coronary, 14 in the left circumflex arteries. The analyzed time series were: RR(t) (beat-to-beat consecutive intervals); RRLF(t), LF[0.04-0.15 Hz]; RRHF(t), HF[0.15-0.4 Hz]. For RR(t), results showed less regularity behavior in pre-PTCA than during PTCA and less regularity during PTCA than in post-PTCA. In RRLF(t) and RRHF(t), lower regularity was presented during PTCA than in pre-PTCA. These findings suggested that MIA was able to provide a tool allowing assessment of ANS response during PTCA.

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 (I) and the order of the entropies (q). Statistical analysis with repeated measures and discriminant analysis revealed statistically significant differences (p-value