P. Caminal

Characterization of QT interval adaptation to RR interval changes and its use as a risk-stratifier of arrhythmic mortality in amiodarone-treated survivors of acute myocardial infarction

A new method is proposed to evaluate the dynamics of QT interval adaptation in response to heart rate (HR) changes. The method considers weighted averages of RR intervals (R~R~) preceding each cardiac beat to express RR interval history accounting for the influence on repolarization duration. A global optimization algorithm is used to determine the weight distribution leading to the lowest regression residual when curve fitting the [QT, R~R~] data using a patient- specific regression model. From the optimum weight distribution, a memory lag L/sub 90/ is estimated, expressing the delay in the QT adaptation to HR changes. On average, RR intervals of the past 150 beats (approximately 2.5 min) are required to model the QT response accurately. From a clinical point of view, the interval of the initial tens of seconds to one minute seems to be most important in the majority of cases. A measure of the optimum regression residual (r/sub opt/) has been calculated, discriminating between post-myocardial infarction patients at high and low risk of arrhythmic death while on treatment with amiodarone. A similar discrimination has been achieved with a variable expressing the character of QT lag behind the RR interval dynamics.

Adaptive Hermite models for ECG data compression: performance and evaluation with automatic wave detection

An orthogonal transformation based on Hermite functions is proposed as a method for ECG data compression. In order to apply the procedure four signal windows are selected in each beat, corresponding to the principal ECG features: P wave, QRS complex, ST segment and T wave. The performance of the method is analysed calculating the compression ratio (CR) and the relative mean-square error (MSE) in each window and in the whole beat. The method has been applied to ECG records from MIT/BIH arrhythmia database. In normal beats with a CR=11.6, the authors have obtained a MSE=(0.09/spl plusmn/0.02)%. In ECG signals containing normal beats and multiform PVCs a MSE=(0.56/spl plusmn/3.41)% is obtained, with a CR=10.3. To analyse the clinical applicability of the method, the algorithm was evaluated with an automatic wave detection program. Differences between the automatic measures in the original signal and in the reconstructed signal were compared and shown a good agreement.<<ETX>>

Adaptive feature extraction for QRS classification and ectopic beat detection

An adaptive system based on the Hermite functions is proposed to adaptively estimate and track the QRS complexes in the electrocardiogram (ECG) signal with few and nonredundant parameters. The system is based on the multiple-input adaptive linear combiner, where the primary input signal is the succession of the QRS complexes, and the reference inputs are the Hermite functions. The weight vector becomes an estimation of the coefficients that represent the QRS complex in the Hermite function base. To adapt these weights the LMS algorithm is used. The authors incorporated a procedure to adaptively estimate a width parameter (b) that best fits each QRS complex. Applications of this system to classify QRS in case of ECG signals affected by the phenomenon of bigeminy and to detect ectopic beats using the b parameter are presented. In both cases correct pattern classification was obtained.<<ETX>>

Linear and nonlinear heart rate variability risk stratification in heart failure patients

Chronic heart failure (CHF) is a major and growing public health concern (~23 million people worldwide) with five-year survival rates of 25% in men and 38% in women. Objective of this study was to investigate whether linear and nonlinear heart rate variability (HRV) indices enhance risk prediction in patients with CHF. To discriminate between low risk (stable condition, N = 459) and high risk (cardiac death, N = 50) CHF patient groups, nonlinear indices from compression entropy (CE), detrended fluctuation analysis (DFA), symbolic dynamics (SD) and standard linear HRV analysis were calculated from 24 h Holter ECG recordings. Indices from nonlinear dynamics (CE, DFA, SD: p < 0.001) contribute together with clinical parameters NYHA and LVEF to an enhanced risk stratification in CHF patients.

Study of the respiratory pattern variability in patients during weaning trials

Mechanical ventilators are used to provide life support in patients with respiratory failure. One of the challenges in intensive care is the process of weaning from mechanical ventilation. We studied the differences in respiratory pattern variability between patients capable of maintaining spontaneous breathing during weaning trials and patients that fail to maintain spontaneous breathing. The respiratory pattern was characterized by the following time series: inspiratory time (T/sub I/), expiratory time (T/sub E/), breath duration (T/sub Tot/), tidal volume (V/sub T/), fractional inspiratory time (T/sub I//T/sub Tot/), mean inspiratory flow (V/sub T//T/sub I/), respiratory frequency (f), and rapid shallow breathing index (f/V/sub T/). The variational activity of breathing was partitioned into autoregressive, periodic and white noise fractions. Patients with unsuccessful trial presented a tendency to higher values of gross variability of V/sub T//T/sub I/ and f/V/sub T/, and lower values of T/sub I/. The autocorrelation coefficients tended to present higher values for T/sub I/, T/sub I//T/sub Tot/ and V/sub T//T/sub I/. During both successful and unsuccessful T-tube test uncorrelated random behavior constituted > 75% of the variance of each time breath components and represented 50 to 70% in the breath component related to V/sub T/. Correlated behavior represented 6 to 21% in time components and 28 to 50% in component related to V/sub T/.

Information Flow to Assess Cardiorespiratory Interactions in Patients on Weaning Trials

Nonlinear processes of the autonomic nervous system (ANS) can produce breath-to-breath variability in the pattern of breathing. In order to provide assess to these nonlinear processes, nonlinear statistical dependencies between heart rate variability and respiratory pattern variability are analyzed. In this way, auto-mutual information and cross-mutual information concepts are applied. This information flow analysis is presented as a short-term non linear analysis method to investigate the information flow interactions in patients on weaning trials. 78 patients from mechanical ventilation were studied: Group A of 28 patients that failed to maintain spontaneous breathing and were reconnected; Group B of 50 patients with successful trials. The results show lower complexity with an increase of information flow in group A than in group B. Furthermore, a more (weakly) coupled nonlinear oscillator behavior is observed in the series of group A than in B

Time-frequency representation of the HRV: a tool to characterize sudden cardiac death in hypertrophy cardiomyopathy patients

In the present work, Heart Rate Variability (HRV) is described by time-frequency representation (TFR), in order to stratify hypertrophic cardiomyopathy (HCM) patients with increasing risk of suffering sudden cardiac death (SCD). TFR and Fast Fourier Transform (FFT) analysis are also compared. The analysis is based on three frequency bands: VLF, 0-0.04 Hz; LF, 0.04-0.15 Hz; and KF, 0.15-0.45 Hz. New variables based on the instantaneous frequency and energy functions using TFR and time-domain analysis allow to discriminate HCM patients with high risk and low risk of SCD (p<0.05). Results shown that TFR analysis of the HRV seems to present more robustness than FFT analysis in order to characterize HRV.

Analysis of stationarity and statistical changes in myographic signals from respiratory muscles

Spectral analysis of myographic signals from respiratory muscles is a promising non-invasive technique to study respiratory diseases. However, it requires that the signal be at least weakly stationary. Electromyographic (EMG) and vibromyographic (VMG) signals are related to electrical and mechanical activity, respectively. Local stationarity of the signals from an accessory respiratory muscle is evaluated by means of the reverse arrangement test. A methodology for change detection and to analyze the evolution of the stationarity during the respiratory cycle in myographic signals is also presented. These studies are performed in healthy subjects and patients with chronic obstructive pulmonary disease. Local stationarity decreases with the increase of the level of ventilation and when a maintained exercise goes forward. High levels of ventilation and respiratory muscle fatigue produce statistical changes in myographic signals.

Analysis of vibromyographic and electromyographic signals from sternomastoid muscle in COPD patients

Analysis of the respiratory muscles activity is a promising technique for diagnosis of respiratory diseases, such as chronic obstructive pulmonary disease (COPD). The sternomastoid muscle was selected to study the activity of respiratory muscles, due to its accessibility. This work proposes the analysis of vibromyographic and electromyographic signals from the sternomastoid muscle, in order to evaluate the muscle function in a ventilatory test. Spectral analysis was performed. The Welch periodogram and autoregressive models were used. Results from a group of 5 patients with COPD are shown at different levels of inspiratory loads. Activity of sternomastoid muscle was measured by means of root-mean-square (rms) values and mean and median frequencies, and they were related to level of severity of COPD patients.<<ETX>>

Adaptive Filtering Of Event-related Bioelectric Signals

We present an adaptive filter to estimate the deterministic component of event-related bioelectric signals. This filter removes the noise uncorrelated with a signal time-locked to a stimulus. A description of the filter structure, convergence time and improvement of the signal-to-noise ratio are presented. A simulation study is camed out to evaluate its performance and compare it with signal averaging. The simulation results agree with the theoretical analysis. The former was selected to detect His-Purkinje signals and ventricular late potentials [2]. This filter is capable of filtering muscle noise, but not the 50-Hz interference due to its periodicity. The latter was used to cancel 50-Hz interference and other bioelectrical signals in electrocardiography [l]. Recently, an application of this adaptive noise canceller was presented to detect P-waves in the ECG, by an adaptive QRS-T cancellation [3]. In this work we propose the next adaptive filter for event-related signals (fig. 1). The signal we want to study (dk) is the primary signal