Pere Caminal

Methods derived from nonlinear dynamics for analysing heart rate variability

Methods from nonlinear dynamics (NLD) have shown new insights into heart rate (HR) variability changes under various physiological and pathological conditions, providing additional prognostic information and complementing traditional time- and frequency-domain analyses. In this review, some of the most prominent indices of nonlinear and fractal dynamics are summarized and their algorithmic implementations and applications in clinical trials are discussed. Several of those indices have been proven to be of diagnostic relevance or have contributed to risk stratification. In particular, techniques based on mono- and multifractal analyses and symbolic dynamics have been successfully applied to clinical studies. Further advances in HR variability analysis are expected through multidimensional and multivariate assessments. Today, the question is no longer about whether or not methods from NLD should be applied; however, it is relevant to ask which of the methods should be selected and under which basic and standardized conditions should they be applied.

Acoustic analysis of snoring sound in patients with simple snoring and obstructive sleep apnoea

Snoring, a symptom which may indicate the presence of the obstructive sleep apnoea syndrome (OSA), is also common in the general population. Recent studies have suggested that the acoustic characteristics of snoring sound may differ between simple snorers and OSA patients. We have studied a small number of patients with simple snoring and OSA, analysing the acoustic characteristics of the snoring sound. Seventeen male patients, 10 with OSA (apnoea/hypopnoea index (AHI) 26.2 events x h(-1)) and seven simple snorers (AHI 3.8 events x h(-1)), were studied. Full night polysomnography was performed and the snoring sound power spectrum was analysed. Spectral analysis of snoring sound showed the existence of two different patterns. The first pattern was characterized by the presence of a fundamental frequency and several harmonics. The second pattern was characterized by a low frequency peak with the sound energy scattered on a narrower band of frequencies, but without clearly identified harmonics. The seven simple snorers and two of the 10 patients with OSA (AIH 13 and 14 events x h(-1), respectively) showed the first pattern. The rest of the OSA patients showed the second pattern. The peak frequency of snoring was significantly lower in OSA patients, with all but one OSA patient and only one simple snorer showing a peak frequency below 150 Hz. A significant negative correlation was found between AHI and peak and mean frequencies of the snoring power spectrum (p<0.0016 and p<0.0089, respectively). In conclusion, this study demonstrates significant differences in the sound power spectrum of snoring sound between subjects with simple snoring and obstructive sleep apnoea patients.

Adaptive filter for event-related bioelectric signals using an impulse correlated reference input: comparison with signal averaging techniques

An adaptive impulse correlated filter (AICF) for event-related signals that are time-locked to a stimulus is presented. This filter estimates the deterministic component of the signal and removes the noise uncorrelated with the stimulus, even if this noise is colored, as in the case of evoked potentials. The filter needs two inputs: the signal (primary input) and an impulse correlated with the deterministic component (reference input). The LMS algorithm is used to adjust the weights in the adaptive process. It is shown that the AICF is equivalent to exponentially weighted averaging (FWA) when using the LMS algorithm. A quantitative analysis of the signal-to-noise ratio improvement, convergence, and misadjustment error is presented. A comparison of the AICF with ensemble averaging (EA) and moving window averaging (MWA) techniques is also presented. The adaptive filter is applied to real high-resolution ECG signals and time-varying somatosensory evoked potentials.<<ETX>>

Alignment methods for averaging of high-resolution cardiac signals: a comparative study of performance

A comparative study of the performance of three alignment methods (the double-level method, a new time-delay estimation method based on normalized integrals, and matched filtering) is presented. A real signal and additive random noise for several signal-to-noise ratios (SNRs) are selected to make an ensemble of computer-simulated beats. The relation between the standard deviation of temporal misalignment versus SNR is discussed. A second study with real ECG signals is also presented. Several morphologies of QRS and P waves are tested. The results are in agreement with the computer simulation study. Nevertheless, the power spectrum of the noise process can affect the results. Matched filter estimation has been tested in the presence of power line interferences (50 Hz), with poor results. An application of the three alignment methods as a function of the SNR is proposed. The new time-delay estimation method has been observed to be robust, even in the presence of nonwhite noise.<<ETX>>

Adaptive estimation of QRS complex wave features of ECG signal by the Hermite model

The most characteristic wave set in ECG signals is the QRS complex. Automatic procedures to classify the QRS are very useful in the diagnosis of cardiac dysfunctions. Early detection and classification of QRS changes are important in realtime monitoring. ECG data compression is also important for storage and data transmission. An Adaptive Hermite Model Estimation System (AHMES) is presented for on-line beat-to-beat estimation of the features that describe the QRS complex with the Hermite model. The AHMES is based on the multiple-input adaptive linear combiner, using as inputs the succession of the QRS complexes and the Hermite functions, where a procedure has been incorporated to adaptively estimate a width related parameter b. The system allows an efficient real-time parameter extraction for classification and data compression. The performance of the AHMES is compared with that of direct feature estimation, studying the improvement in signal-to-noise ratio. In addition, the effect of misalignment at the QRS mark is shown to become a neglecting low-pass effect. The results allow the conditions in which the AHMES improves the direct estimate to be established. The application is shown, for subsequent classification, of the AHMES in extracting the QRS features of an ECG signal with the bigeminy phenomena. Another application is highlighted that helps wide ectopic beats detection using the width parameter b.

Refined Multiscale Entropy: Application to 24-h Holter Recordings of Heart Period Variability in Healthy and Aortic Stenosis Subjects

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.

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

Heart rate turbulence predicts all-cause mortality and sudden death in congestive heart failure patients

BACKGROUND Abnormal heart rate turbulence (HRT) has been documented as a strong predictor of total mortality and sudden death in postinfarction patients, but data in patients with congestive heart failure (CHF) are limited. OBJECTIVE The aim of this study was to evaluate the prognostic significance of HRT for predicting mortality in CHF patients in New York Heart Association (NYHA) class II-III. METHODS In 651 CHF patients with sinus rhythm enrolled into the MUSIC (Muerte Subita en Insuficiencia Cardiaca) study, the standard HRT parameters turbulence onset (TO) and slope (TS), as well as HRT categories, were assessed for predicting total mortality and sudden death. RESULTS HRT was analyzable in 607 patients, mean age 63 years (434 male), 50% of ischemic etiology. During a median follow up of 44 months, 129 patients died, 52 from sudden death. Abnormal TS and HRT category 2 (HRT2) were independently associated with increased all-cause mortality (HR: 2.10, CI: 1.41 to 3.12, P <.001 and HR: 2.52, CI: 1.56 to 4.05, P <.001; respectively), sudden death (HR: 2.25, CI: 1.13 to 4.46, P = .021 for HRT2), and death due to heart failure progression (HR: 4.11, CI: 1.84 to 9.19, P <.001 for HRT2) after adjustment for clinical covariates in multivariate analysis. The prognostic value of TS for predicting total mortality was similar in various groups dichotomized by age, gender, NYHA class, left ventricular ejection fraction, and CHF etiology. TS was found to be predictive for total mortality only in patients with QRS > 120 ms. CONCLUSION HRT is a potent risk predictor for both heart failure and arrhythmic death in patients with class II and III CHF.

Cerebral state index during propofol anesthesia : A Comparison with the Bispectral Index and the A-Line ARX Index

Background:The objective of this study was to prospectively test the Cerebral State Index designed for measuring the depth of anesthesia. The Cerebral State Index is calculated using a fuzzy logic combination of four subparameters of the electroencephalographic signal. The performance of the Cerebral State Index was compared with that of the Bispectral Index and the A-Line ARX Index. Methods:This study applied raw data from two previously published clinical protocols. The patients in protocol 1 were given a continuous propofol infusion, 300 ml/h, until 80% of burst suppression occurred. In protocol 2, a stepwise increased target-controlled infusion of propofol was administered to patients until loss of response to noxious stimuli while the Observer’s Assessment of Alertness and Sedation was registered every 4 min. The Cerebral State Index was calculated off-line from the recorded electroencephalographic data. The Spearman rank correlation coefficient between electronic indices and the effect site concentration of propofol was calculated along with the prediction probability of each index to predict the Observer’s Assessment of Alertness and Sedation level. Results:The Spearman rank correlation coefficients between the Cerebral State Index, Bispectral Index, and A-Line ARX Index and the propofol effect site concentration were −0.94, −0.89, and −0.82, respectively, in protocol 1, whereas the prediction probability values between the Cerebral State Index, Bispectral Index, and A-Line ARX Index and the Observer’s Assessment of Alertness and Sedation score in protocol 2 were 0.92, 0.93, and 0.91, respectively. Conclusion:The Cerebral State Index detects well the graduated levels of propofol anesthesia when compared with the propofol effect site concentration and the Observer’s Assessment of Alertness and Sedation score.

Comparison of conventional averaged and rapid averaged, autoregressive-based extracted auditory evoked potentials for monitoring the hypnotic level during propofol induction.

Background The extraction of the middle latency auditory evoked potentials (MLAEP) is usually done by moving time averaging (MTA) over many sweeps (often 250–1,000), which could produce a delay of more than 1 min. This problem was addressed by applying an autoregressive model with exogenous input (ARX) that enables extraction of the auditory evoked potentials (AEP) within 15 sweeps. The objective of this study was to show that an AEP could be extracted faster by ARX than by MTA and with the same reliability. Methods The MTA and ARX methods were compared with the Modified Observers Assessment of Alertness and Sedation Scale (MOAAS) in 15 patients scheduled for cardiac surgery and anesthetized with propofol. The peak amplitudes and latencies were recorded continuously for the MTA- and ARX-extracted AEP. An index, AAI, was derived from the ARX-extracted AEP as well. Results The best predictors of the awake and anesthetized states, in terms of the prediction probability, Pk, were the AAI (Pk [SE] = 0.93 [0.01]) and Na-Pa amplitude (MTA, Pk [SE] = 0.89 [0.02]; ARX, Pk [SE] = 0.87[0.02]). When comparing the AAI at the MOAAS levels 5–3 versus 2–0, significant differences were achieved. During the transitions from awake to asleep, the ARX-extracted AEP were obtained with significantly less delay than the MTA-extracted AEP (28.4 s vs. 6 s). Conclusion The authors conclude that the MLAEP peaks and the AAI correlate well to the MOAAS, whether extracted by MTA or ARX, but the ARX method produced a significantly shorter delay than the MTA.