QT Series Research Articles

Model-based Estimators of QT Series Time Delay in Following Heart-Rate Changes.

Sudden cardiac death is the leading cause of death among cardiovascular diseases. Markers for patient risk stratification focusing on QT-interval dynamics in response to heart-rate (HR) changes can be characterized in terms of parametric QT to RR dependence and QT/RR hysteresis. The QT/RR hysteresis can be quantified by the time delay the QT interval takes to accommodate for the HR changes. The exercise stress test has been proposed as a proper test, with large HR dynamics, to evaluate the QT/RR hysteresis. The present study aims at evaluating several time-delay estimators based on noise statistic (Gaussian or Laplacian) and HR changes profile at stress test (gradual transition change). The estimator's performance was assessed on a simulated QT transition contaminated by noise and in a clinical study including patients affected by coronary arteries disease (CAD). As expected, the Laplacian and Gaussian estimators yield the best results when noise follows the respective distribution. Further, the Laplacian estimator showed greater discriminative power in classifying different levels of cardiac risk in CAD patients, suggesting that real data fit better the Laplacian distribution than the Gaussian one. The Laplacian estimator appears to be the choice for time-delay estimation of QT/RR hysteresis lag in response to HR changes in stress test.Clinical Relevance-The proposed time-delay estimator of QT/RR hysteresis lag improves its significance as biomarkers for coronary artery diseases risk stratification.

QT interval time lag in response to heart rate changes during stress test for Coronary Artery Disease diagnosis

Background: Slow adaptation of the QT interval to abrupt changes in heart rate (HR) can enhance ventricular heterogeneity and has been suggested as a marker of arrhythmic risk. Most investigations on QT rate adaptation lag have been performed in response to step-like HR changes. However, abrupt HR changes are difficult to induce or observe in ECG recordings under ambulatory conditions. Objective:We aim to evaluate the power of indices related to the QT lag in response to ramp-like HR changes in stress test to assess CAD risk. Methods:We quantified the lag between the actual QT series and the memoryless expected QT series, which was obtained by fitting a hyperbolic regression model to the instantaneous QT and HR measurements in stages where their behavior could be assumed stationary. The proposed methodology was applied to analyze ECG stress tests of a subset of 448 patients presenting different risk levels for Coronary Artery Disease (CAD). The QT lag was estimated separately in the exercise and recovery phases. Results:An increase in the estimated QT lag during exercise (from 25 to 36 s) and a decrease during recovery (from 57 to 39 s) were associated with higher CAD risk. The difference between these lags showed significant capacity for CAD risk stratification. Conclusion:The QT lag in response to HR changes can be quantified from a stress test. QT lag values in response to ramp-like HR changes are in ranges comparable to those quantified from abrupt HR changes and show clinical significance to stratify CAD risk.

The role of β-adrenergic stimulation in QT interval adaptation to heart rate during stress test.

The adaptation lag of the QT interval after heart rate (HR) has been proposed as an arrhythmic risk marker. Most studies have quantified the QT adaptation lag in response to abrupt, step-like changes in HR induced by atrial pacing, in response to tilt test or during ambulatory recordings. Recent studies have introduced novel methods to quantify the QT adaptation lag to gradual, ramp-like HR changes in stress tests by evaluating the differences between the measured QT series and an estimated, memoryless QT series obtained from the instantaneous HR. These studies have observed the QT adaptation lag to progressively reduce when approaching the stress peak, with the underlying mechanisms being still unclear. This study analyzes the contribution of β-adrenergic stimulation to QT interval rate adaptation in response to gradual, ramp-like HR changes. We first quantify the QT adaptation lag in Coronary Artery Disease (CAD) patients undergoing stress test. To uncover the involved mechanisms, we use biophysically detailed computational models coupling descriptions of human ventricular electrophysiology and β-adrenergic signaling, from which we simulate ventricular action potentials and ECG signals. We characterize the adaptation of the simulated QT interval in response to the HR time series measured from each of the analyzed CAD patients. We show that, when the simulated ventricular tissue is subjected to a time-varying β-adrenergic stimulation pattern, with higher stimulation levels close to the stress peak, the simulated QT interval presents adaptation lags during exercise that are more similar to those measured from the patients than when subjected to constant β-adrenergic stimulation. During stress test recovery, constant and time-varying β-adrenergic stimulation patterns render similar adaptation lags, which are generally shorter than during exercise, in agreement with results from the patients. In conclusion, our findings support the role of time-varying β-adrenergic stimulation in contributing to QT interval adaptation to gradually increasing HR changes as those seen during the exercise phase of a stress test.

Quantification of the acute effect of a low dose of red wine by nonlinear measures of RR and QT interval series in healthy subjects

The measures of nonlinear properties of RR interval and QT interval time series are sensitive to physiologically- or pathologically-induced complexity/regularity changes, but were not used to estimate the effect of alcohol intake. We wanted to examine the potential of these measures to quantify the acute effect of a low dose of red wine in healthy subjects. In separate experiments, fourteen young volunteers drank 200ml of red wine and a control drink with equal concentration of ethanol. ECG in supine position was recorded 20min before and 60min after drink intake. RR interval and QT interval series were extracted from ECG and we calculated variability, scaling exponents (α1 and α2) and sample entropy (SampEn) for both series. Systolic and diastolic blood pressures (BP) were measured every 10min. The immediate effect of both the drinks was equal: HR, BP and QT variability exhibited a sudden increase and then a decrease. However, the prolonged effect of wine and the control drink was different. Wine decreased both BP (p<0.05) and reduced complexity of RR and QT series (increased scaling exponents and decreased SampEn). The control drink prolonged QT and RR intervals (p<0.05). These results point out that the nonlinear properties of RR and QT interval series could be used to differentiate the effect of wine and ethanol. Changes in RR and QT interval series induced by a low dose of red wine are more detectable by methods that quantify the structure of the series than by methods that quantify their variability.

Filtering approach based on empirical mode decomposition improves the assessment of short scale complexity in long QT syndrome type 1 population.

This study assesses the complexity of heart period (HP) and QT variability series through sample entropy (SampEn) in long QT syndrome type 1 individuals. In order to improve signal-to-noise ratio SampEn was evaluated over the original series (SampEn0) and over the residual computed by subtracting the first oscillatory mode identified by empirical mode decomposition (SampEn(EMD1R)). HP and QT interval were continuously extracted during daytime (2:00-6:00 PM) from 24 hour Holter recordings in 14 non mutation carriers (NMCs) and 34 mutation carriers (MCs) subdivided in 11 asymptomatic (ASYMP) and 23 symptomatic (SYMP). Both NMCs and MCs belonged to the same family line. While SampEn0 did not show differences among the three groups, Samp(EnEMD1R) assessed over the QT series significantly decreased in ASYMP subjects. SampEn(EMD1R) identified a possible factor (i.e. the lower short scale QT complexity) that might contribute to the different risk profile of the ASYMP group.

The quantification of the QT-RR interaction in ECG signal using the detrended fluctuationanalysis and ARARX modelling.

In this paper, the detrended fluctuation analysis DFA is used to investigate and quantify the QT-RR interaction in different pathologic cases in order to distinguish between them. The study is carried out on the ECG signals of MIT-BIH universal database. Different ECG signals related to cardiac pathological cases are concerned with this study. These are: Premature Ventricular Contraction (PVC) (9 cases), Right Bundle Branch Block (RBBB) (4 cases), Left Bundle Branch Block (LBBB) (2 cases), Atrial Premature Beat (APB) (4 cases), Paced Beat (PB) (4 cases), and other pathologic cases with different severity (10 cases). All this cases are compared to the 15 normal cases. The obtained results show that the DFA can identify the healthy subject from the pathologic cases according to the values of the scaling exponent α. The results indicate that α varies between 0.5 and 1 in all cases which means that there is a long range correlation in RR and QT series. The QT and RR series are also modelled using the ARARX model. The parameters of the model are then extracted. The power spectral density (PSD) is estimated by using these parameters in order to provide further information about the causal interactions within the signals and also to determine the power scaling exponent β. This scaling exponent confirms the relationship between RR and QT intervals in all the studied cases except in APB and PB cases where the behaviour is similar to that of the white noise. The QT variability degrees are calculated and the DFA is applied on it. The obtained results show a long range correlation between RR and QT intervals in all cases and an ambiguity in the APB case. The DFA is compared to the Poincaré method in order to evaluate the algorithm performance using the Fuzzy Sugeno classifier is used for this purpose.

Analysis of the QT-RR variability interactions using the NARMAX model

In this paper a new approach is used in order to evaluate and quantify the interactions between the QT and RR intervals. This is achieved after the identification of the RR and QT series with a hybrid model (the non-linear autoregressive moving average with exogenous input (NARMAX)). This identification follows two steps: the first is a linear parametric identification corresponding to the MA model, whereas the second is a non-linear identification using the NARX model. The power spectral density PSD of RR and QT is computed by using the monovariate part of this model (MA model). The QT-related RR series is obtained by using the bivariate part corresponding to the NARX model and its PSD is determined by using the autoregressive method. Then a cross-spectral and the coherence function were determined in order to confirm the obtained results. Different heart pathology cases were selected to evaluate the approach: the normal case, the cases which represent long QT intervals and some other cases which represent short QT intervals. They were taken from the MIT BIH database. The results show that every case illustrates two frequencies; the first in the low frequency band LF and the second in the high frequency band HF. In the normal case and long QT interval cases, the LF was predominating in the QT, RR and in QT-related RR power spectral density PSD. In the short QT interval cases the HF was much larger in all cases. The obtained results were compared to the poincaré plot method which confirms it; however, the NARMAX model can distinguish between normal and pathologic cases with a great precision (p < 0.001). In addition, the QT variability index QTVI is computed and represented by a box plot which expresses the relationship between QT and RR intervals. The QTVI shows a large variability in the short QT interval cases, whereas it shows a small and a negative variability in the long QT interval case.

Effects of caffeine on heart rate and QT variability during sleep

Administration of caffeine in the evening produces poor sleep. Patients with insomnia have characteristic electrocardiogram (ECG) changes, including increased heart rate (HR), increased sympathetic activity, and decreased parasympathetic activity. Fifteen young adult normal subjects slept in the laboratory for several nights prior to randomization into a caffeine protocol where subjects received caffeine 400 mg 30 min prior to one night of sleep and placebo randomly prior to another night. ECG was sampled at a rate of 500 Hz and recorded onto a PC. Data samples of 256-s periods of the ECG trace were taken from wake (before sleep), stage II, and REM for placebo and caffeine conditions. A peak detection algorithm was used to identify the R-R intervals (in milliseconds) from the ECG. A common QT variability algorithm was used to find the QT interval for each beat using the time-stretch model. The powers for HR and QT series were integrated in the bands of LF (low frequency: 0.04-0.15 Hz) and HF (high frequency: 0.15-0.5 Hz) bands. There was a significant caffeine by sleep stage interaction for LF/HF ratios (F = 4.0; df = 2, 18; P = .04). LF/HF ratios were significantly higher during REM following caffeine administration. There was also a significant caffeine by sleep stage interaction for QTvi (QT variability normalized for mean QT interval divided by HR variability normalized for mean HR; F = 5.6; df = 2, 12; P = .02). QTvi was also significantly higher during REM following caffeine administration. The higher LF/HF ratios and QTvi during REM are most likely due to the sympathetic effects of caffeine. These findings suggest that excessive caffeine intake may result in adverse cardiovascular events in vulnerable subjects.