QT Interval Time Series Research Articles

Enhanced Automated Diagnosis of Coronary Artery Disease Using Features Extracted From QT Interval Time Series and ST–T Waveform

There is a growing interest in automated diagnosis of coronary artery disease (CAD) with the application of machine learning (ML) methods to the body surface electrocardiograph (ECG). Although prior studies have documented associations of CAD with increased QT variability and ST–T segment abnormalities such as T-wave inversion and ST-segment elevation or depression, their efficacy in automated CAD detection has not been fully investigated. To validate their usefulness, a dataset containing related clinical characteristics and 5-min single-lead ECGs of 107 healthy controls and 93 CAD patients was first constructed. Based on this dataset, simultaneous analyses were then conducted in five scenarios, in which different ML algorithms were applied to classify the two groups with various features derived from the RR and QT interval time-series and ST–T segment waveforms. Compared with utilizing features obtained from the RR interval time-series, better classification results were achieved utilizing that obtained from the QT interval time-series. The classification results were elevated with combining utilization of features derived from both the RR and QT interval time-series. By further fusing features extracted from ST–T segment waveforms, the best performance was achieved with 96.16% accuracy, 95.75% sensitivity, and 96.40% specificity. Based the best performance, an automated CAD detection system was developed with extreme gradient boosting, an ensemble ML algorithm, and the residual neural network, namely, a deep learning method. The results of this study support the potential of information derived from the QT interval time-series and ST–T segment waveforms in ECG-based automated CAD detection.

Recording duration and short-term reproducibility of heart rate and QT interval variability in patients with myocardial infarction

Beat-to-beat variability of the QT interval (QTV) measured on surface ECG has emerged as a potential marker for ventricular repolarization instability and has been used along with heart rate variability (HRV) to predict arrhythmic risk. Since measurement modalities of QTV have not been standardized, the objective of this study was to investigate the effect of ECG recording duration on QTV as well as HRV. Using a database of 30 min ECG recorded from 500 patients with acute myocardial infraction during rest, we extracted RR and QT interval time series and estimated different HRV and QTV metrics over windows of varying length. Analysis of variance (ANOVA) and intra-class correlation analyses were computed to investigate the effect of recording length on consistency and short-term reproducibility of HRV and QTV variables. Good consistency (non-significant ANOVA results) and short-term reproducibility (intra-class correlation coefficients >0.8) were demonstrated for all but standard deviation based metrics when at least 200 beats were included in the estimation. In conclusion, QTV can be quantified from resting ECG with good short-term consistency and reproducibility that is comparable to that of HRV.

Entropy Analysis of RR and QT Interval Variability during Orthostatic and Mental Stress in Healthy Subjects

Autonomic activity affects beat-to-beat variability of heart rate and QT interval. The aim of this study was to explore whether entropy measures are suitable to detect changes in neural outflow to the heart elicited by two different stress paradigms. We recorded short-term ECG in 11 normal subjects during an experimental protocol that involved head-up tilt and mental arithmetic stress and computed sample entropy, cross-sample entropy and causal interactions based on conditional entropy from RR and QT interval time series. Head-up tilt resulted in a significant reduction in sample entropy of RR intervals and cross-sample entropy, while mental arithmetic stress resulted in a significant reduction in coupling directed from RR to QT. In conclusion, measures of entropy are suitable to detect changes in neural outflow to the heart and decoupling of repolarisation variability from heart rate variability elicited by orthostatic or mental arithmetic stress.

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.

Decoupling of QT interval variability from heart rate variability with ageing

Ageing has been associated with changes in cardiac electrophysiology that result in QT interval prolongation. The effect of age on rate-adaptation dynamics of the QT interval is less well understood. The aim of this study was to assess age-related changes in the temporal relationship between QT and RR interval variability. Resting ECG of 20 young and 20 elderly healthy subjects were analyzed. Beat-to-beat RR and QT interval time series were automatically extracted. Coupling between QT and RR was assessed by means of the QT variability index, coherence in the frequency domain, rate-corrected QT interval, cross-multiscale entropy, information based similarity index and joint symbolic dynamics. In addition to QT interval prolongation (433 ± 31 versus 405 ± 33 ms, p = 0.008), elderly subjects were characterized by a significantly increased QT variability index (−1.26 ± 0.28 versus −1.52 ± 0.22 ms, p < 0.0001), reduced coherence in high (0.11 ± 0.09 versus 0.29 ± 0.14 ms, p = 0.003), and low frequency bands (0.20 ± 0.16 versus 0.49 ± 0.15 ms, p < 0.0001), reduced information domain synchronization index (0.13 ± 0.07 versus 0.19 ± 0.05 ms, p = 0.001) as well as increased entropy and disparity in joint symbolic dynamics of QT and RR interval time series. In conclusion, ageing is associated with decoupling of QT variability from heart rate variability. Complexity analysis in addition to standard metrics may provide additional insight.

Multiscale entropy and detrended fluctuation analysis of QT interval and heart rate variability during normal pregnancy

Pregnancy leads to physiological changes in various parameters of the cardiovascular system. The aim of this study was to investigate longitudinal changes in the structure and complexity of heart rate variability (HRV) and QT interval variability during the second half of normal gestation. We analysed 30-min high-resolution ECGs recorded monthly in 32 pregnant women, starting from the 20th week of gestation. Heart rate and QT variability were quantified using multiscale entropy (MSE) and detrended fluctuation analyses (DFA). DFA of HRV showed significantly higher scaling exponents towards the end of gestation (p<0.0001). MSE analysis showed a significant decrease in sample entropy of HRV with progressing gestation on scales 1–4 (p<0.05). MSE analysis and DFA of QT interval time series revealed structures significantly different from those of HRV with no significant alteration during the second half of gestation.In conclusion, pregnancy is associated with increases in long-term correlations and regularity of HRV, but it does not affect QT variability. The structure of QT time series is significantly different from that of RR time series, despite its close physiological dependence.

Beat-to-beat heart rate and QT variability in patients with congestive cardiac failure: blunted response to orthostatic challenge.

Congestive cardiac failure is associated with increased sympathetic activity and impaired baroreflex function. We sought to test the hypothesis that these patients also have blunted response of beat-to-beat QT interval variability during orthostatic challenge. We compared beat-to-beat heart rate and QT interval data in 17 patients with congestive cardiac failure and 17 age-matched normal controls in supine normal breathing, supine controlled breathing, and standing controlled breathing conditions. The ECG data were acquired in lead II configuration at a sampling rate of 1000 Hz. Supine controlled breathing was associated with an increase in spectral HF power (0.15-0.5 Hz) of HR and QT interval time series compared to spontaneous breathing condition only in controls. While there were significant changes in HR, HR LF power, HR LF/HF ratios, and QT variability measures in standing posture in controls, there were no such changes in patients. This impairment of postural changes of HR variability is most likely due to an impaired baroreceptor function in patients with congestive heart failure. The etiology of this is likely due to an increased cardiac sympathetic and a decreased vagal function. However, the relationship of postural changes in beat-to-beat QT interval variability and baroreflex need further investigation.

Effect of nortriptyline and paroxetine on measures of chaos of heart rate time series in patients with panic disorder

Tricyclic antidepressants have notable cardiac side effects, and this issue has become important due to the recent reports of increased cardiovascular mortality in patients with depression and anxiety. Several previous studies indicate that serotonin reuptake inhibitors (SRIs) do not appear to have such adverse effects. Apart from the effects of these drugs on routine 12-lead ECG, the effects on beat-to-beat heart rate (HR) and QT interval time series provide more information on the side effects related to cardiac autonomic function. In this study, we evaluated the effects of two antidepressants, nortriptyline ( n=13), a tricyclic, and paroxetine ( n=16), an SRI inhibitor, on HR variability in patients with panic disorder, using a measure of chaos, the largest Lyapunov exponent (LLE) using pre- and posttreatment HR time series. Our results show that nortriptyline is associated with a decrease in LLE of high frequency (HF: 0.15–0.5 Hz) filtered series, which is most likely due to its anticholinergic effect, while paroxetine had no such effect. Paroxetine significantly decreased sympathovagal ratios as measured by a decrease in LLE of LF/HF. These results suggest that paroxetine appears to be safer in regards to cardiovascular effects compared to nortriptyline in this group of patients.

Nonlinear measures of QT interval series: novel indices of cardiac repolarization lability: MEDqthr and LLEqthr

In this study, we investigated nonlinear measures of chaos of QT interval time series in 28 normal control subjects, 36 patients with panic disorder and 18 patients with major depression in supine and standing postures. We obtained the minimum embedding dimension (MED) and the largest Lyapunov exponent (LLE) of instantaneous heart rate (HR) and QT interval series. MED quantifies the system's complexity and LLE predictability. There was a significantly lower MED and a significantly increased LLE of QT interval time series in patients. Most importantly, nonlinear indices of QT/HR time series, MEDqthr (MED of QT/HR) and LLEqthr (LLE of QT/HR), were highly significantly different between controls and both patient groups in either posture. Results remained the same even after adjusting for age. The increased LLE of QT interval time series in patients with anxiety and depression is in line with our previous findings of higher QTvi (QT variability index, a log ratio of QT variability corrected for mean QT squared divided by heart rate variability corrected for mean heart rate squared) in these patients, using linear techniques. Increased LLEqthr (LLE of QT/HR) may be a more sensitive tool to study cardiac repolarization and a valuable addition to the time domain measures such as QTvi. This is especially important in light of the finding that LLEqthr correlated poorly and nonsignificantly with QTvi. These findings suggest an increase in relative cardiac sympathetic activity and a decrease in certain aspects of cardiac vagal function in patients with anxiety as well as depression. The lack of correlation between QTvi and LLEqthr suggests that this nonlinear index is a valuable addition to the linear measures. These findings may also help to explain the higher incidence of cardiovascular mortality in patients with anxiety and depressive disorders.

QT and RR interval variability and spectral characteristics in response to physiologic autonomic stimulation

Purpose:The purposes of this study were to compare the magnitude and phase between the RR interval and QT interval variability in the frequency domain. Methods:Twenty four, 12-13 year old healthy males were randomly selected. At resting state and for 5 minutes, ECGs were obtained, and they were digitized to 1000Hz. After measurement of RR interval, QT interval variability was measured using template matching strategy. After normalization of the RR and QT interval time series, power spectral and cross spectral analysis were performed. From each of the time series, low(0.04-0.15 hertz) and high(0.15-0.4 hertz) frequency power were measured. From the phase spectrum, the phases and time lags between the two time series at each of the two frequency range were calculated. Results:The average of RR interval and QT interval was 616.0±71.0, 364.0±47.0 msec, respectively. Their normalized lowand highfrequency power was 4.4±7.9 NU (normalized unit), 0.1±0.1 NU (p<0.005), and 11.0±30.0 NU, 0.3±0.3 (NU, p<0.005), respectively. The phase differences and resulting time lags between the two interval were -0.5±0.4 πradian (-0.9 seconds) and -0.2 ±0.3 πradian (-0.4 seconds) in the lowand high-frequency range, respectively. Conclusion:During resting state, when compared to RR interval, QT interval oscillates in significantly lower amplitude in both lowand highfrequency ranges. However, the oscillations precede those of the RR interval 0.9 seconds and 0.4 seconds, respectively. (Korean Circulation J 2000;30(12):1507-1514)