Validity of the Polar V800 monitor for measuring heart rate variability in mountain running route conditions.

PURPOSE: This study was conducted to test, in mountain running route conditions, the accuracy of the Polar V800™ monitor as a suitable device for monitoring the heart rate variability (HRV) of runners. METHOD: Eighteen healthy subjects ran a route that included a range of running slopes such as those encountered in trail and ultra-trail races. The comparative study of a V800 and a Holter SEER 12 ECG Recorder™ included the analysis of RR time series and short-term HRV analysis. A correction algorithm was designed to obtain the corrected Polar RR intervals. Six 5-min segments related to different running slopes were considered for each subject. RESULTS: The correlation between corrected V800 RR intervals and Holter RR intervals was very high (r = 0.99, p < 0.001), and the bias was less than 1 ms. The limits of agreement (LoA) obtained for SDNN and RMSSD were (- 0.25 to 0.32 ms) and (- 0.90 to 1.08 ms), respectively. The effect size (ES) obtained in the time domain HRV parameters was considered small (ES < 0.2). Frequency domain HRV parameters did not differ (p > 0.05) and were well correlated (r ≥ 0.96, p < 0.001). CONCLUSION: Narrow limits of agreement, high correlations and small effect size suggest that the Polar V800 is a valid tool for the analysis of heart rate variability in athletes while running high endurance events such as marathon, trail, and ultra-trail races.

Evaluation of very low amplitude intra-QRS potentials during the initial minutes of acute transmural myocardial ischemia.

INTRODUCTION: Low-level electrocardiographic changes from depolarization wavefront may accompany acute myocardial ischemia. The purpose of this study was to assess the changes of microvolt amplitude intra-QRS potentials induced by elective percutaneous coronary interventions (PCI). METHODS: Fifty-seven patients with balloon inflation periods ranging from 3.1 to 7.3 minutes (4.9±0.7 min) were studied. Nine leads continuous high-resolution ECG before and during PCI were recorded and signal-averaged. Abnormal intra-QRS at microvolt level (µAIQP) were obtained using a signal modeling approach. µAIQP, R-wave amplitude and QRS duration were measured in the processed ECG during baseline and PCI episodes. RESULTS: The mean µAIQP amplitude significantly decreased for each of the standard 12 leads at the PCI event respect to baseline. Left anterior descending artery (LAD) occlusion resulted in a decrease µAIQP in both the precordial leads and the limb leads, while right coronary (RCA) and left circumflex (LCx) arteries occlusions mainly affected limb leads. R-wave amplitude increased during PCI in RCA and LCx groups in lead III but decreased in the precordial leads, while the amplitude decreased in the LAD group in lead III. The average duration of the QRS augmented in groups RCA and LCx but not in the LAD group. CONCLUSIONS: Abnormal intra-QRS potentials at the level of µV provide an excellent tool to characterize the very-low amplitude fragmentation of the QRS complex and its changes due to ischemic injuries. µAIQP shows promise as a new ECG index to measure electrophysiologic changes associated with acute myocardial ischemia.

Assessment of autonomic control of the heart during transient myocardial ischemia.

INTRODUCTION: In the presence of coronary artery obstruction, complex cardiovascular reflexes may lead to changes in heart rate and even to the precipitation of malignant arrhythmias. The autonomic nervous system (ANS) has traditionally been considered to be "balanced" between continuously interacting sympathetic and parasympathetic outflows. The purpose of this study was to assess ANS control of the heart during prolonged coronary balloon occlusion procedures of one of the major coronary arteries. METHODS: R-R intervals were obtained from continuous electrocardiographic data of 90 patients undergoing selective percutaneous coronary interventions (PCI) with balloon inflation periods ranging from 3 to 10 minutes (4.7 ± 1.1 minutes). Three 3-minute stages were chosen: (1) preinflation (baseline), (2) from the start of occlusion (PCI), and (3) immediately post deflation. The dynamics of the ANS was evaluated by heart rate variability analysis using standard time and frequency domain indices and the short-term fractal-like index (α(1)). RESULTS: During PCI, time and frequency domain measures related to vagal control decreased significantly with respect to baseline (significantly in left anterior descending [LAD] artery occlusions). During the postdeflation stage, heart rate variability and high-frequency power increased (P < .01) in the group with right coronary artery occlusions, whereas a marked sympathetic increase, as assessed by an increase (P < .01) of normalized low-frequency power and the low/high-frequency ratio was observed in the LAD group after balloon deflation. Fractal index α(1) decreased during the PCI period but increased significantly after balloon deflation. CONCLUSIONS: Significant changes in autonomic control of heart rate that were a function of the affected artery occurred during and after coronary artery occlusions. Occlusion of the LAD resulted in a significant reduction of vagal activity and a decrease of the short-term fractal index during PCI and a marked sympathetic response after postdeflation. However, a marked increment of vagal activity between the occlusion stage and postdeflation period was found in the right coronary artery group. These results may relate the site of the occlusion and lack of blood supply to different parts of the left ventricle.

MISS: a non-linear methodology based on mutual information for genetic association studies in both population and sib-pairs analysis.

MOTIVATION: Finding association between genetic variants and phenotypes related to disease has become an important vehicle for the study of complex disorders. In this context, multi-loci genetic association might unravel additional information when compared with single loci search. The main goal of this work is to propose a non-linear methodology based on information theory for finding combinatorial association between multi-SNPs and a given phenotype. RESULTS: The proposed methodology, called MISS (mutual information statistical significance), has been integrated jointly with a feature selection algorithm and has been tested on a synthetic dataset with a controlled phenotype and in the particular case of the F7 gene. The MISS methodology has been contrasted with a multiple linear regression (MLR) method used for genetic association in both, a population-based study and a sib-pairs analysis and with the maximum entropy conditional probability modelling (MECPM) method, which searches for predictive multi-locus interactions. Several sets of SNPs within the F7 gene region have been found to show a significant correlation with the FVII levels in blood. The proposed multi-site approach unveils combinations of SNPs that explain more significant information of the phenotype than their individual polymorphisms. MISS is able to find more correlations between SNPs and the phenotype than MLR and MECPM. Most of the marked SNPs appear in the literature as functional variants with real effect on the protein FVII levels in blood. AVAILABILITY: The code is available at http://sisbio.recerca.upc.edu/R/MISS_0.2.tar.gz

Detrended fluctuation analysis of EEG as a measure of depth of anesthesia.

For several decades, a number of methods have been developed for the noninvasive assessment of the level of consciousness during general anesthesia. In this paper, detrended fluctuation analysis is used to study the scaling behavior of the electroencephalogram as a measure of the level of consciousness. Three indexes are proposed in order to characterize the patient state. Statistical analysis demonstrates that they allow significant discrimination between the awake, sedated and anesthetized states. Two of them present a good correlation with established indexes of depth of anesthesia. The scaling behavior has been found related to the depth of anesthesia and the methodology allows real-time implementation, which enables its application in monitoring devices.

Changes of autonomic information flow due to idiopathic dilated cardiomyopathy.

Risk stratification of patients with idiopathic dilated cardiomyopathy (IDC) is an epidemiologically relevant question. But the results based on conventional heart rate variability (HRV) analysis are still unsatisfactory. The adjustments within the cardiovascular system incorporate nonlinear and complex mechanisms of information exchange which may have additional prognostic value. It is an objective of the present work to evaluate the prognostic value of autonomic information flow (AIF) measures in IDC patients compared to conventional HRV measures in a first explorative study. Holter recordings of 32 patients with idiopathic dilated cardiomyopathy (IDC) and 12 normal subjects (NRM) were analyzed. The IDC patients consisted of two groups: 10 high risk (HR) patients, after aborted sudden cardiac death (SCD); 22 low risk (LR) patients, without SCD. Sensitivity, specificity, positive predictive value, negative predictive value and ROC characteristics of a comprehensive set of AIF measures, organized according to the conventional HRV standards, and conventional HRV measures were investigated. The significant risk predictors were evaluated by Spearman's rank correlation. While the only traditional HRV measure discriminating IDC patients from NRM was ln(LF) most of the AIF measures had a discriminatory value. Concerning the prognosis of the IDC patients by conventional HRV we found that SDNN and all frequency band measures (lnHF, lnLF, lnVLF) significantly discriminated HR from LR. Among the AIF measures the time shift related peak decay (PD(dHF)) reflecting the HF band information flow had a prognostic value. PD(dHF) was identified as a promising candidate which might improve the predictive value of traditional HRV analysis, predominantly represented by SDNN. A subsequent comprehensive clinical study is necessary to validate this hypothesis.

Characterization and classification of patients with different levels of cardiac death risk by using Poincaré plot analysis.

Cardiac death risk is still a big problem by an important part of the population, especially in elderly patients. In this study, we propose to characterize and analyze the cardiovascular and cardiorespiratory systems using the Poincaré plot. A total of 46 cardiomyopathy patients and 36 healthy subjets were analyzed. Left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF > 35%, 16 patients), and high risk (HR: LVEF ≤ 35%, 30 patients) of heart attack. RR, SBP and TTot time series were extracted from the ECG, blood pressure and respiratory flow signals, respectively. Parameters that describe the scatterplott of Poincaré method, related to short- and long-term variabilities, acceleration and deceleration of the dynamic system, and the complex correlation index were extracted. The linear discriminant analysis (LDA) and the support vector machines (SVM) classification methods were used to analyze the results of the extracted parameters. The results showed that cardiac parameters were the best to discriminate between HR and LR groups, especially the complex correlation index (p = 0.009). Analising the interaction, the best result was obtained with the relation between the difference of the standard deviation of the cardiac and respiratory system (p = 0.003). When comparing HR vs LR groups, the best classification was obtained applying SVM method, using an ANOVA kernel, with an accuracy of 98.12%. An accuracy of 97.01% was obtained by comparing patients versus healthy, with a SVM classifier and Laplacian kernel. The morphology of Poincaré plot introduces parameters that allow the characterization of the cardiorespiratory system dynamics.

Relation of heart rate turbulence to severity of heart failure.

The aim of this study was to evaluate the association between heart rate turbulence (HRT) parameters and clinical, biochemical, echocardiographic, and electrocardiographic measures of heart failure (HF) in a large, prospectively enrolled population of patients with HF to determine whether HRT could be considered a marker of HF advancement and progression, giving insight into hemodynamic changes as well as changes of the autonomic nervous system. In 487 patients with HF (mean age 63 years), the following tests were performed: 12-lead surface electrocardiography, echocardiography, chest x-rays, N-terminal-pro-brain natriuretic peptide levels, and 24-hour Holter monitoring for HRT and heart rate variability analyses. Most patients were in New York Heart Association class II (82%) and had a mean left ventricular (LV) ejection fraction of 37 +/- 14%. Both HRT parameters, but especially turbulence slope, were significantly correlated with clinical indexes of HF (the third heart sound, peripheral edemas, jugular distension, and pulmonary congestion). Patients in New York Heart Association class III had significantly lower turbulence slopes and greater turbulence onset values than those in class II. Significant correlations were found between HRT parameters and the LV ejection fraction as well as with LV diameters. HRT parameters were significantly correlated with N-terminal-pro-brain natriuretic peptide levels (r = -0.47, p <0.001 for turbulence slope). Multivariate analyses showed that abnormal HRT parameters were independent predictors of HF severity measured by New York Heart Association class III and a LV ejection fraction <40%. In conclusion, the findings indicate that in patients with HF, HRT reflects well the severity of HF and associated LV dysfunction, which were verified in this study by a series of established clinical, echocardiographic, and biochemical parameters.

Non-linear dynamic analysis of the cardiac rhythm during transient myocardial ischemia.

Coronary artery occlusions related to myocardial ischemia drive cardiac control system reactions that may lead to heart failure. The purpose of this study was to assess the autonomic nervous system (ANS) response during prolonged percutaneous transluminal coronary angioplasty (PTCA). Continuous ECG data were acquired from 50 patients before and during PTCA, with occlusions in the left anterior descending, left circumflex or right coronary artery. Heart rate variability (HRV) was analyzed for 3-min segments of the R-R interval signal obtained from ECG data. The ANS behavior was evaluated by HRV analysis using fractal-like indices. The fractal scalar exponent alpha(1) and power-law slope beta decreased considerably during PTCA. This indicates that significant reactions of autonomic control of the heart rate occurred during coronary artery occlusions, with a reduction in complexity of the ANS.

Compression entropy contributes to risk stratification in patients with cardiomyopathy.

Sudden cardiac death (SCD) is a leading cause of mortality with an incidence of 3 million cases per year worldwide. Therapies for patients who have survived an SCD episode or have a high risk of developing lethal ventricular arrhythmia are well established and depend mainly on risk stratification. In this study we investigated the suitability of the non-linear measure compression entropy (Hc) for improved risk prediction in cardiac patients. We recorded 24-h Holter ECG for 300 patients with congestive heart failure (CHF). During a mean follow-up period of 12 months, 32 patients died due to a cardiac event. Hc depends on the compression parameters window length w and buffer length b, which were optimised by analysing a subgroup of patients. Compression entropies based on the beat-to-beat interval (BBI) were subsequently calculated and compared with standard heartrate variability parameters. Statistical analysis revealed significant differences between high- and low-risk CHF patients in standard HRV measures, as well as compression entropy based on the BBI (cardiac death, p = 0.005; SCD, p = 0.02). In conclusion, the implementation of non-linear compression entropy analysis in multivariate analysis seems to be useful for enhanced risk stratification of cardiac death, especially SCD, in ischaemic cardiomyopathy patients.

Complex autonomic dysfunction in cardiovascular, intensive care, and schizophrenic patients assessed by autonomic information flow.

BACKGROUND: The cardiovascular control system is mediated by mechanisms acting at different time scales, such as heart period, vagal, sympathetic, and other slower controllers. Since these elements are interrelated in a complex manner, classical control theory fails and information-based description, based on autonomic information flow (AIF) functions, is appropriate. We investigated the hypothesis that AIF functions of typical time scales specifically characterize autonomic dysfunction and prognosis. MATERIALS AND METHODS: Holter recordings of patients with multiple organ dysfunction syndrome (MODS) (26 survivors, 10 non-survivors), heart failure (13 low risk, 13 high risk of cardiac arrest), idiopathic dilated cardiomyopathy (IDC) (26 low risk, 11 high risk), after abdominal aorta surgery (AAS) [32 with length of stay in hospital (LOS) >7 days; 62 with LOS < or =7 days] or with schizophrenia (n=20) were assessed and compared to 20 control subjects. RESULTS: We found different AIF time scales discriminating risk. AIF measures of heart beat period had predominant prognostic value in heart failure patients, those of vagal communication in MODS and IDC, and those of long-term communication after AAS. Schizophrenic patients were discriminated from controls by vagally mediated communication. CONCLUSION: Different time scales of AIF represent specific pathophysiological aspects of altered complex autonomic control (communication) and consequently have predictive implications.

Analysis of blood pressure signal in patients with different ventricular ejection fraction using linear and non-linear methods.

Changes in the left ventricle function produce alternans in the hemodynamic and electric behavior of the cardiovascular system. A total of 49 cardiomyopathy patients have been studied based on the blood pressure signal (BP), and were classified according to the left ventricular ejection fraction (LVEF) in low risk (LR: LVEF>35%, 17 patients) and high risk (HR: LVEF≤35, 32 patients) groups. We propose to characterize these patients using a linear and a nonlinear methods, based on the spectral estimation and the recurrence plot, respectively. From BP signal, we extracted each systolic time interval (STI), upward systolic slope (BPsl), and the difference between systolic and diastolic BP, defined as pulse pressure (PP). After, the best subset of parameters were obtained through the sequential feature selection (SFS) method. According to the results, the best classification was obtained using a combination of linear and nonlinear features from STI and PP parameters. For STI, the best combination was obtained considering the frequency peak and the diagonal structures of RP, with an area under the curve (AUC) of 79%. The same results were obtained when comparing PP values. Consequently, the use of combined linear and nonlinear parameters could improve the risk stratification of cardiomyopathy patients.

Assessment of sedation-analgesia by means of Poincaré analysis of the electroencephalogram.

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.

Correlation dimension analysis of heart rate variability in patients with dilated cardiomyopathy.

A correlation dimension analysis of heart rate variability (HRV) was applied to a group of 55 patients with dilated cardiomyopathy (DCM) and 55 healthy subjects as controls. The 24-h RR time series for each subject was divided into segments of 10,000 beats to determine the correlation dimension (CD) per segment. A study of the influence of the time delay (lag) in the calculation of CD was performed. Good discrimination between both groups (p<0.005) was obtained with lag values of 5 or greater. CD values of DCM patients (8.4+/-1.9) were significantly lower than CD values for controls (9.5+/-1.9). An analysis of CD values of HRV showed that for healthy people, CD night values (10.6+/-1.8) were significant greater than CD day values (9.2+/-1.9), revealing a circadian rhythm. In DCM patients, this circadian rhythm was lost and there were no differences between CD values in day (8.8+/-2.4) and night (8.9+/-2.1).

Evaluation of acceleration and deceleration cardiac processes using phase-rectified signal averaging in healthy and idiopathic dilated cardiomyopathy subjects.

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 (ANS) in HR patients.

Cardiorespiratory and cardiovascular interactions in cardiomyopathy patients using joint symbolic dynamic analysis.

Cardiovascular diseases are the first cause of death in developed countries. Using electrocardiographic (ECG), blood pressure (BP) and respiratory flow signals, we obtained parameters for classifying cardiomyopathy patients. 42 patients with ischemic (ICM) and dilated (DCM) cardiomyopathies were studied. The left ventricular ejection fraction (LVEF) was used to stratify patients with low risk (LR: LVEF>35%, 14 patients) and high risk (HR: LVEF≤ 35%, 28 patients) of heart attack. RR, SBP and TTot time series were extracted from the ECG, BP and respiratory flow signals, respectively. The time series were transformed to a binary space and then analyzed using Joint Symbolic Dynamic with a word length of three, characterizing them by the probability of occurrence of the words. Extracted parameters were then reduced using correlation and statistical analysis. Principal component analysis and support vector machines methods were applied to characterize the cardiorespiratory and cardiovascular interactions in ICM and DCM cardiomyopathies, obtaining an accuracy of 85.7%.

Measuring heart rate variability by means of information entropies based on Choi-Williams distribution.

The Shannon entropy theory was applied to the Choi-Williams time-frequency distribution (CWD) of cardiac time series (RR series) in order to extract entropy information in both time and frequency domains. From this distribution, four indexes were defined: (1) instantaneous partial entropy; (2) spectral partial entropy; (3) instantaneous complete entropy; (4) spectral complete entropy. These indexes were used for analyzing the heart rate variability of ischemic cardiomyopathy patients (ICM) with different sudden cardiac death risk. The results have shown that the values of these indexes tend to decrease, with different proportion, when the severity of pathological condition increases. Statistical differences (p-value < 0.0005) of these indexes were found comparing low risk and high risk of cardiac death during night and between daytime and nighttime periods of ICM patients. Finally, these indexes have demonstrated to be useful tools to quantify the different complex components of the cardiac time series.

Symbolic dynamics to discriminate healthy and ischaemic dilated cardiomyopathy populations: an application to the variability of heart period and QT interval.

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.

Regularity of cardiac rhythm as a marker of sleepiness in sleep disordered breathing.

AIM: The present study aimed to analyse the autonomic nervous system activity using heart rate variability (HRV) to detect sleep disordered breathing (SDB) patients with and without excessive daytime sleepiness (EDS) before sleep onset. METHODS: Two groups of 20 patients with different levels of daytime sleepiness -sleepy group, SG; alert group, AG- were selected consecutively from a Maintenance of Wakefulness Test (MWT) and Multiple Sleep Latency Test (MSLT) research protocol. The first waking 3-min window of RR signal at the beginning of each nap test was considered for the analysis. HRV was measured with traditional linear measures and with time-frequency representations. Non-linear measures -correntropy, CORR; auto-mutual-information function, AMIF- were used to describe the regularity of the RR rhythm. Statistical analysis was performed with non-parametric tests. RESULTS: Non-linear dynamic of the RR rhythm was more regular in the SG than in the AG during the first wakefulness period of MSLT, but not during MWT. AMIF (in high-frequency and in Total band) and CORR (in Total band) yielded sensitivity > 70%, specificity >75% and an area under ROC curve > 0.80 in classifying SG and AG patients. CONCLUSION: The regularity of the RR rhythm measured at the beginning of the MSLT could be used to detect SDB patients with and without EDS before the appearance of sleep onset.

Prediction of Nociceptive Responses during Sedation by Linear and Non-Linear Measures of EEG Signals in High Frequencies.

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 Pk 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.