T-wave End Research Articles

Cerebral Ischemia and Reperfusion-Induced Changes in Left Ventricular Function and Electrocardiogram in Mice

Background: Stroke may produce functional and electrical heart disturbances. The underlying characteristics and mechanisms have not been fully elucidated. Objectives: To evaluate whether acute cerebral ischemia (I) and reperfusion (R) may cause cardiac dysfunction and electrocardiographic alterations in an experimental mice model. Methods: Male mice that underwent cerebral ischemia and reperfusion (I/R) were evaluated by electrocardiography (ECG) and echocardiography. Heart rate, corrected QT (QTc) interval, T-wave peak to T-wave end (Tp-Te) interval, left ventricular ejection fraction (LVEF), shortening fraction (SF) and isovolumetric relaxation time (IVRT) were analyzed. Cerebral infarct size was calculated, and neurological deficit was assessed with the Longa scale. Results: Twenty-four hours after R, a statistically significant decrease in LVEF (I/R: 66.5±1.5% vs. sham: 74.3±0.9%; p=0.002) and in SF (I/R: 42.9±1.7% vs sham: 52.3±1.7%; p=0.004) was observed. QTc interval prolongation was observed during I/R (baseline: 125.1±4.3 ms; 60 min after I: 143.8±5.2 ms; 24 h after R: 170.3±5.8 ms; p=0.002). Tp-Te interval was not prolonged during I (baseline: 25.9±1.3 ms vs. 60 min after I: 23.8±1.4 ms; p=0.999) but it was prolonged during R (24 h after R: 32.0±2.3 ms; p=0.049). Cerebral infarct size was 34.9±2.5% and survival in the I/R group was 43.3%. Conclusion: Acute cerebral ischemia induces mild left ventricular dysfunction and disturbances in ventricular repolarization which intensify within the first 24 hours after reperfusion.

Performance Evaluation of QT-RR Adaptation Time Lag Estimation in Exercise Stress Testing.

Slower adaptation of the QTinterval to sudden changes in heart rate has been identified as a risk marker of ventricular arrhythmia. The gradual changes observed in exercise stress testing facilitates the estimation of the QT-RR adaptation time lag. The time lag estimation is based on the delay between the observed QTintervals and the QTintervals derived from the observed RRintervals using a memoryless transformation. Assuming that the two types of QTinterval are corrupted with either Gaussian or Laplacian noise, the respective maximum likelihood time lag estimators are derived. Estimation performance is evaluated using an ECG simulator which models change in RR and QT intervals with a known time lag, muscle noise level, respiratory rate, and more. The accuracy of T-wave end delineation and the influence of the learning window positioning for model parameter estimation are also investigated. Using simulated datasets, the results show that the proposed approach to estimation can be applied to any changes in heart rate trend as long as the frequency content of the trend is below a certain frequency. Moreover, using a proper position of the learning window for exercise so that data compensation reduces the effect of nonstationarity, a lower mean estimation error results for a wide range of time lags. Using a clinical dataset, the Laplacian-based estimator shows a better discrimination between patients grouped according to the risk of suffering from coronary artery disease. Using simulated ECGs, the performance evaluation of the proposed method shows that the estimated time lag agrees well with the true time lag.

Abstract 11749: Identifying Infants With Long QT Syndrome Type 1: Looking Beyond the QTc

Background: Abnormal repolarization in long QT syndrome type 1 (LQT1) is characterized by QT prolongation and abnormal T-waves. In the absence of familial disease, LQT1 is easily missed in infants as the QTc is often normal, exposing children to life-threatening arrhythmia. Hypothesis: T-wave morphology metrics are superior to QTc for diagnosis of LQT1. Aim: Compare quantifiable aspects of T-wave morphology and QTc to differentiate infants with and without LQT1. Methods: Infants aged 0.5-6 months with a pathogenic/likely pathogenic KCNQ1 variant were compared to age/sex-matched controls in a 1:2 ratio. Measurements were made in leads II/V5. T-wave morphology was assessed by J-point to T-wave peak (J-Tp) and Tp to T-wave end (Tp-Te). Bazett’s formula was used to correct for heart rate and Te defined using the tangent method. Results: We identified 34 LQT1 infants and 68 controls. The median (IQR) QTc was 457 (430-476) ms for LQT1 cases and 402 (392-420) ms for controls (p<0.001). The 98%tile QTc for controls was 448 ms and 13/34 (38%) LQT1 cases were below this value. In lead II, LQT1 cases had a significantly longer rate corrected J-Tp compared to controls, 294 (272-312) ms v. 222 (211-234) ms (p <0.001) and shorter Tp-Te, 71 (60-80) ms v. 85 (75-95) ms (p<0.001). The J-Tp/Tp-Te ratio was 0.25 in LQT1 cases and 0.39 in controls (p<0.001). Findings were similar in V5. V5 rate corrected J-Tp best discriminated between LQT1 cases and controls (ROC AUC 0.99, optimal cut point ≥256ms) and outperformed QTc (AUC 0.92). This remained true in those with QTc <98%tile (AUC 0.98). Measurements in a subset of 30 infants made by 3 ECG readers showed superior reliability for J-Tp in leads II and V5 (ICC coefficient 0.92) compared to QTc (0.88). Conclusions: Nearly 40% of infants with LQT1 have a normal a QTc, yet a significantly abnormal and quantifiable T-wave morphology. J-Tp and Tp-Te are reliable and reproducible measurements and should be included in the assessment of potential infantile LQT1.

Implementation of Wavelet-Transform-Based Algorithms in an FPGA for Heart Rate and RT Interval Automatic Measurements in Real Time: Application in a Long-Term Ambulatory Electrocardiogram Monitor.

Cardiovascular diseases are currently the leading cause of death worldwide. Thus, there is a need for non-invasive ambulatory (Holter) ECG monitors with automatic measurements of ECG intervals to evaluate electrocardiographic abnormalities of patients with cardiac diseases. This work presents the implementation of algorithms in an FPGA for beat-to-beat heart rate and RT interval measurements based on the continuous wavelet transform (CWT) with splines for a prototype of an ambulatory ECG monitor of three leads. The prototype's main elements are an analog-digital converter ADS1294, an FPGA of Xilinx XC7A35T-ICPG236C of the Artix-7 family of low consumption, immersed in a low-scale Cmod-A7 development card integration, an LCD display and a micro-SD memory of 16 Gb. A main state machine initializes and manages the simultaneous acquisition of three leads from the ADS1294 and filters the signals using a FIR filter. The algorithm based on the CWT with splines detects the QRS complex (R or S wave) and then the T-wave end using a search window. Finally, the heart rate (60/RR interval) and the RT interval (from R peak to T-wave end) are calculated for analysis of its dynamics. The micro-SD memory stores the three leads and the RR and RT intervals, and an LCD screen displays the beat-to-beat values of heart rate, RT interval and the electrode connection. The algorithm implemented on the FPGA achieved satisfactory results in detecting different morphologies of QRS complexes and T wave in real time for the analysis of heart rate and RT interval dynamics.

PO-04-209 A NOVEL MACHINE LEARNING (ML) ALGORITHM TO MEASURE THE CORRECTED QT INTERVAL FROM MOBILE ECGS WITH HIGH ACCURACY AND PRECISION

The heart rate-corrected QT interval (QTc) is an important feature of the electrocardiogram (ECG), as it is an important prognosticator for ventricular arrhythmia and sudden cardiac death. The 12-lead (12L) ECG is used most often for assessment of QTc, and algorithms that have extended QTc detection to mobile ECGs with fewer than 12 leads are prone to wide sample size deviation. For clinical applications requiring serial QTc monitoring, such as titration of QTc-prolonging drugs, both accuracy and precision are critical. The aim was to develop a novel machine-learning (ML) algorithm that measures QTc from mobile ECG data formats with high accuracy as well as precision. The acceptance criteria was defined as error and standard deviation within 20 milliseconds (ms), the human interobserver variability observed for manual ECG measurements (gold standard). The ML algorithm was trained using a database of 13,000 cardiologist-annotated 12L ECG heartbeats, including the following steps: 1) pre-processing including resampling to 300 Hz, 2) normalization of amplitudes using min-max scaling 3) noisy beats with excessive baseline wander were discarded, and 4) applying TabNet and XGBoost models for detection of the end of T-Wave. This ML model for detection of the T-wave endpoint was merged with a signal processing method for detection of the Q-point. The algorithm was then validated on a subset of 1,239 cardiologist-annotated heartbeats, sufficient to assess strong agreement between software and manual methods (95% CI ± 0.2 std, Bland-Altman approach). Results showed high AUC, sensitivity, and specificity for detecting QTc above clinically meaningful thresholds. Mean QTc error was 5.24±7.22 ms, meeting predefined acceptance criteria. The ML algorithm was then applied to a dataset of 150 6L mECGs (AliveCor; Mountain View, CA), which comprise an all-comer population of a combination of healthy (∼40%), outpatient clinic (30%), and hospitalized patients (30%), with error 13.7±7.78 ms, again meeting acceptance criteria. Algorithm validation surpassed the defined acceptance criteria and demonstrated that the algorithm can provide and potentially surpass human QTc measurement capabilities. By enabling automated, dynamic QTc analysis, the algorithm offered the precision required for outpatient use.

Dual assessment of abnormal cardiac electrical dispersion and diastolic dysfunction for early detection of the epileptic heart condition

BackgroundPeople with epilepsy (PWE) are at increased risk for premature death due to many factors. Sudden unexpected death in epilepsy (SUDEP) is among the most important causes of death in these individuals and possibly, sudden cardiac death (SCD) in epilepsy is also as important. The possibility of concurrent derangement in electrical and mechanical cardiac function, which could be a marker of early cardiac involvement in PWE, has not been investigated in that population. MethodsElectrical dispersion indices (T-wave peak to T-wave end, TpTe; QT dispersion, QTd; QT interval corrected for heart rate, QTc) were analyzed in patients with pharmacoresistant temporal lobe epilepsy and compared to a control group. The electromechanical relationship between those indices and echocardiographic parameters were further assessed in PWE. ResultsIn 19 PWE and 21 controls, we found greater TpTe and QTd in PWE (TpTe: 91.6 ± 16.4 ms vs. 65.2 ± 12.1 ms, p < 0.0001; and QTd: 45.3 ± 13.1 ms vs. 19 ± 6.2 ms, p < 0.0001, respectively). QTc was similar between PWE and controls (419.2 ± 31.4 ms vs. 435.1 ± 31.4 ms, p = 0.12). In multivariate linear regression, TpTe, QTc, and epilepsy duration were related to left ventricular mass; QTc was associated with left atrial volume; QTc, the number of seizures per month, epilepsy duration and antiseizure medication explained 81% of E/A mitral wave Doppler ratio. ConclusionsThis is the first report to demonstrate concurrent electrical dispersion and diastolic dysfunction in PWE. These noninvasive biomarkers could prove useful in early detection of the “Epileptic Heart” condition.

Abstract 10266: T-Wave Features Predict Functional Worsening in Children With Pulmonary Arterial Hypertension: Machine Learning Insights From Standard ECG

Introduction: Various T-wave characteristics have been shown to be predictive of ventricular diastolic dysfunction and sudden cardiac death in adults. However, specific T-wave morphologies and their predictive potential in pediatric patients is unknown. Hypothesis: Machine learning derived T-wave characteristics will be different between children with pulmonary arterial hypertension (PAH) and healthy controls. Furthermore, T-wave characteristics will be predictive of clinical functional worsening in PAH patients. Methods: Principal component analysis (PCA) was applied to ECGs collected for children with PAH (n=155) at the time of diagnostic catheterization and matched healthy controls (n=47) to describe basic T-wave characteristics. Patients (&lt;18 y/o) with PAH were defined as a mean pulmonary pressure &gt;25 mmHg in the absence of obstructive left heart disease. Interpreted T-wave features were then compared between PAH and control groups. Identified T-wave measurements underwent survival analysis using standardized PAH functional class worsening as a clinical endpoint. Results: Identified T-wave principal components were 1) T-wave height and 2) early or late incidence of T-wave peak (FIGURE- 1A). The second principal component was associated with PAH diagnostic specificity (94%). Time from T-wave peak to T-wave end (TpTe) as measured in leads V4 to V6 was significantly higher in children with PAH (all P &lt; 0.001) (FIGURE - 1B). V4 derived TpTe value &gt; 132 ms and V5 derived TpTe value &gt; 135 ms were associated with the higher probability of clinical functional worsening (P = 0.018 and P = 0.002, respectively) (FIGURE - 1C). Conclusions: T-wave features derived from standard ECG are 1) different between children with PAH and their healthy peers and 2) predictive of clinical functional worsening.

Effects of β-blockers on ventricular repolarization documented by 24-hour electrocardiography in long QT syndrome type 2

Long QT syndrome (LQTS) is an inherited arrhythmia disorder characterized by ventricular repolarization abnormalities and a risk of sudden cardiac death. The electrophysiological components generating the high risk of arrhythmias in LQTS are prolonged repolarization, increased dispersion of repolarization, and early afterdepolarizations, which are clinically estimated as QT interval, T-wave peak to T-wave end (TPE) interval, and T2/T1-wave amplitude ratio, respectively. In experimental LQTS type 2 (LQT2) models, β-blockers decrease dispersion of repolarization and prevent early afterdepolarizations. In clinical studies in patients with LQT2 , β-blockers are more effective against exercise-induced than arousal-induced cardiac events. The aim of the study was to investigate the effects of β-blocker therapy on repolarization properties in LQT2. QT and TPE intervals and maximal T2/T1-wave amplitude ratios recorded by 24-hour electrocardiograms before and during β-blocker therapy were evaluated in 25 patients with LQT2. β-Blocker therapy decreased the maximal T2/T1-wave amplitude ratio from 2.9 ± 1.1 to 1.8 ± 0.7 (P < .001), but did not change the pause-induced T2/T1-wave amplitude ratio. Under medication, abrupt maximal TPE intervals were shorter at heart rates of ≥75 beats/min and maximal QT intervals were shorter at a heart rate of 100 beats/min. β-Blockers stabilize ventricular repolarization in LQT2 by reducing electrocardiographic early afterdepolarizations and by reducing abrupt prolongation of electrocardiographic dispersion of repolarization and ventricular repolarization duration at elevated heart rates. The effect of β-blockers on pause-induced electrocardiographic early afterdepolarizations is weak. The findings provide electrocardiographic explanation for the protective effects of β-blockers against exercise-induced cardiac events in LQT2.

RBHHM: A novel remote cardiac cycle detection model based on heartbeat harmonics

Cardiac cycle detection methods based on radar sensing have made marked progress in the last decade. In this study, a novel harmonic distribution based non-contact cardiac cycle detection method is innovatively proposed to extract heartbeat intervals and rhythm sequences from radar signals. Initially, we exploit the bimodal Gaussian distribution model of the frequency domain and the Shannon energy to separate heartbeat harmonics from radar signals, which preserve instantaneous variation features of signal components. Furthermore, we cluster harmonics into three types of waves based on K-means. The peak positions of the three waves are near locations of the R-peak, T-peak, and T-wave end in the electrocardiograph (ECG) respectively. Thus, the detected peak locations of three waves can be directly used to estimate heartbeat-to-beat intervals. Additionally, we compared three recurrent neural networks (RNNs) using the clustered waves to segmentation sequences of “R-T-R” and “Q-R-S-T-Q”. Specific subsets are classified from the sequences to prove their association with cardiac systolic and diastolic variation. Finally, verification is performed using a clinical dataset. The suitable centre frequency range to use the proposed model is about 3–15 Hz. The performance of the cardiac cycle detection method achieves a mean relative error of 5.34% ± 3.57% for BBIs detection and an accuracy of 95.88% for heartbeat rhythm segmentation. The experimental results show that our radar-based heartbeat harmonic model (RBHHM) based method has better accuracy than other state-of-the-art cardiac cycle detection methods.

T-wave peak-end interval and ratio of T-wave peak-end and QT intervals: novel arrhythmogenic and survival markers for dogs with myxomatous mitral valve disease

Introduction/objectivesIn the past few years, novel markers such as the interval between the peak and the end of T-wave (Tpte) and Tpte/QT ratio have been shown to have high sensitivity for ventricular arrhythmias and mortality. We analyzed these and other parameters of ventricular repolarization, such as QT interval, QT interval corrected for heart rate (QTc), and QT dispersion (QTd) in dogs with myxomatous mitral valve disease (MMVD). Additionally, we investigated their relationship with the progression of the disease, echocardiographic parameters, and ventricular arrhythmias and assessed their prognostic value with development of clinical signs or mortality as the final outcome. Animals, materials and methodsEpidemiological, clinical, echocardiographic, and electrocardiographic data were obtained from 236 dogs with MMVD and 15 healthy dogs. Prognostic and survival information was also recorded for the MMVD group. All ventricular repolarization indices were measured in 10 lead electrocardiographic recordings. ResultsWith the exception of the QT interval, most repolarization markers increased along with the frequency of arrhythmias and with the progression of MMVD. The parameters that best identified ventricular arrhythmias (AUC > 0.7) were Tpte (aVR, rV2, average rV2–V10, average rV2–V4) and Tpte/QT (II, aVR, rV2). In survival analysis, statistically significant markers with the highest differences in median survival were Tpte (maximum of any lead, maximum rV2–V10), QTc aVR, and Tpte rV2. ConclusionTpte and Tpte/QT are good non-invasive markers for clinical risk stratification in dogs with MMVD.

Robust T-End Detection via T-End Signal Quality Index and Optimal Shrinkage.

An automatic accurate T-wave end (T-end) annotation for the electrocardiogram (ECG) has several important clinical applications. While there have been several algorithms proposed, their performance is usually deteriorated when the signal is noisy. Therefore, we need new techniques to support the noise robustness in T-end detection. We propose a new algorithm based on the signal quality index (SQI) for T-end, coined as tSQI, and the optimal shrinkage (OS). For segments with low tSQI, the OS is applied to enhance the signal-to-noise ratio (SNR). We validated the proposed method using eleven short-term ECG recordings from QT database available at Physionet, as well as four 14-day ECG recordings which were visually annotated at a central ECG core laboratory. We evaluated the correlation between the real-world signal quality for T-end and tSQI, and the robustness of proposed algorithm to various additive noises of different types and SNR’s. The performance of proposed algorithm on arrhythmic signals was also illustrated on MITDB arrhythmic database. The labeled signal quality is well captured by tSQI, and the proposed OS denoising help stabilize existing T-end detection algorithms under noisy situations by making the mean of detection errors decrease. Even when applied to ECGs with arrhythmia, the proposed algorithm still performed well if proper metric is applied. We proposed a new T-end annotation algorithm. The efficiency and accuracy of our algorithm makes it a good fit for clinical applications and large ECG databases. This study is limited by the small size of annotated datasets.

Ventricular transmural decremental conduction explains short t-peak-t-end interval in “in silico” ECG reconstruction model

Abstract Introduction In chronic hepatic cirrhosis (CHC), T-wave peak to T-wave end interval (TPTE) has shown prognostic value for survival and liver transplantation. Altered cellular ionic regulatory systems may produce decremental conduction in ventricular endocardial to epicardial activation wavefront propagation and ECG waveforms, particularly the T-wave. It was investigated whether ventricular transmural activation wavefront conduction may affect T-wave shape and impact TPTE duration, using “in silico” ECG reconstruction. Methods Mammalian-derived ventricular endocardial to epicardial AP waveforms (APW) were simulated and deployed in 10 discrete layers in homogeneous impedance ventricular wedge-like model. A uniform conduction model was employed to mimic normal heart, in which the speed of propagation of the activation wavefront was nonzero and constant in all layers. In CHC heart, a decremental conduction model was employed, in which the speed of propagation of the activation wavefront was maximal at the endocardial layer and exponentially decayed to greater than zero speed at the epicardial layer. ECG was computed as the sum of dipoles weighted by the inverse of the squared distance to an observation electrode arbitrarily located outside the wedge. One dipole was one layer thick, and its charge was assumed as the time-integral of the current generated by the difference of potential between adjacent APW, from endocardial to epicardial layers. ECG was reconstructed in one axis. Results Two-dimensional transmural APW distribution and respective reconstructed ECGs are presented in Figures 1 and 2. In uniform endocardial to epicardial transmural conduction model, QRS complex was taller and shorter as well as TPTE was larger (Fig. 1) than respective counterparts in decremental conduction model (Fig. 2). Additionally, peak-amplitude of the T-wave as well as the maximal slope of the TPTE were lower in decremental as compared to uniform conduction model. Conclusion Using “in silico” ECG reconstruction, decrementally conducted model of epicardial to endocardial ventricular transmural activation wavefront propagation yields wider and lower amplitude QRS complex as well as shorter TPTE, as compared to respective counterparts in uniformly conducted activation wavefront model. Ventricular transmural decremental conduction model offers an insight into electrophysiological background of ECG findings in CHC. (NCT01433848) Transmural APW distribution vs ECG Funding Acknowledgement Type of funding source: Public Institution(s). Main funding source(s): Universidade do Estado do Rio de Janeiro; Rio de Janeiro, RJ - Brazil

Prolonged Tp-e interval and Tp-e/QT ratio in patients with mitral annulus disjunction

Abstract Background Mitral annulus disjunction (MAD) is an abnormal atrial displacement of the mitral valve leaflet hinge point. MAD has been associated with mitral valve prolapse (MVP) and arrhythmic events. T-wave peak to T-wave end interval (Tp-e) and Tp-e/QTc are electrocardiographic (ECG) indices to predict ventricular tachyarrhythmia and cardiovascular mortality. Purpose We aimed to evaluate ventricular repolarization dispersion by using the Tp-e interval and Tp-e/QT ratio in patients with MAD and healthy controls. Methods A total of 35 patients with MAD (age 40.4±8.2 years; 66% female) and 37 healthy controls (age 37.6±9.5 years; 68% female) were enrolled. All subjects were evaluated by 12 lead standard ECG, 24-hour ambulatory ECG, and transthoracic echocardiography. The MAD distance was measured from the left atrial wall-mitral valve leaflet junction to the top of the left ventricular wall during end-systole in the parasternal long-axis view. The standard 12-lead electrocardiograms were analyzed; QTc, Tp-e and Tp-e/QTc were calculated. The Tp-e interval was defined as the interval from the peak of the T wave to the end of the T wave from precordial leads. Finally, the Tp-e/QT ratio was calculated from these measurements. The normality assumption was checked with the Shapiro-Wilk test and the Mann–Whitney U test was used for inter-group comparisons. Results Tp-e interval (61.9±6.0 ms vs. 77.1±5.9 ms, p&amp;lt;0.001) and Tp-e/QT ratio (0.14±0.01 vs. 0.17±0.02, p&amp;lt;0.001) were significantly prolonged in patients with MAD than in the control group. MVP was present in 24 (69%) patients with MAD. The prevalence of 30 premature ventricular contractions / hour were higher in MAD subgroup without MVP than those MAD with MVP (54.5% vs 16.7%, p=0.041). Mean MAD distance measured by echocardiography was 5.7±1.5 mm. The Spearman's rank-order correlation analyses revealed positive correlations of MAD distance with Tp-e interval (r=0.620, p=0.001) and Tp-e/QT ratio (r=0.372, p=0.028). Conclusions The patients with MAD had a prolonged Tp-e interval and Tp-e/QT ratio compared with normal subjects. Furthermore, this prolongation was well correlated with MAD distance. Patients with MAD, particularly with higher MAD distance, should be followed closely for arrhythmic outcomes. MAD distance and Tp-e interval Funding Acknowledgement Type of funding source: None

Dynamic features of cardiac vector as alternative markers of drug-induced spatial dispersion

IntroductionThe abnormal amplification of ventricular repolarization dispersion (VRD) has long been linked to proarrhythmia risk. Recently, the measure of VRD through electrocardiogram intervals has been strongly questioned. The search for an efficient and non-invasive surrogate marker of drug-induced dispersion effects constitute an urgent research challenge. MethodsHerein, drug-induced ventricular dispersion is generated by d-Sotalol supply in an In-vitro rabbit heart model. A cilindrical chamber simulates the thorax and a multi-electrode net is used to obtain spatial electrocardiographic signals. Cardiac vector dynamics is captured by novel velocity cardiomarkers obtained by quaternion methods. Through statistical analysis and machine learning technics, we compute potential dispersion markers that could define proarrhythmic risk. ResultsThe cardiomarkers with the greatest statistical significance, both obtained from the electrical cardiac vector, were: the QTω, which is the difference between first and last maxima of angular velocity and λ21vT, the roundness of linear velocity. When comparing with the performance of the current standards (89%), this pair was able to correctly separate 21 out of 22 experiments achieving a performance of 95%. Moreover, the QTω computes in a much more robust basis the QT interval, the current index for drug regulation. DiscussionThese velocity markers circumvent the problems of accuratelly finding the fiducial points such as the always tricky T-wave end. Given the high performance they achieved, it is provided a promising outcome for future applications to the detection of anomalous changes of heterogeneity that may be useful for the purposes of torsadogenic toxicity studies.

Variation of Tpeak-end, corrected Tpeak-end, QT, and corrected QT intervals, Tpeak-end/QT, Tpeak-end/corrected QT ratios and heart rate variability according to decades in the healthy male subjects aged between 30 and 79years.

BackgroundHeart rate variability (HRV) is a predictor of cardiac autonomic functions. Ventricular repolarization markers can indicate ventricular arrhythmias. We aimed to evaluate variations of HRV and these repolarization markers in five healthy male groups between age 30 and 79 years according to decades.Materials and MethodsThe study group consisted of 500 healthy male subjects between October 2018 and May 2019. The male subjects were divided into five categories according to their ages. Then, electrocardiograms (ECG), transthoracic echocardiograms (TTE), and treadmill exercise test (TET) were performed. T‐wave peak‐end (Tp‐e) interval was defined as the time between the peak point and end of T‐wave. Tp‐e, corrected Tp‐e (cTp‐e), QT, and corrected QT (QTc) were measured from the resting ECGs and HRV temporal parameters (SDNN, SDNN Index, SDANN Index, RMSSD, sNN50, and pNN50), and HRV frequency parameters (VLF, LF, HF, and LF/HF) were obtained from 24‐hour Holter monitorization recordings. One‐way ANOVA test was used for the differences between the groups. Pearson correlation test was used to determine the correlations between the values of all groups.ResultsConsidering the repolarization parameters, there are significant differences in five groups in terms of Tp‐e interval, but not Tp‐e/QT and Tp‐e/QTc ratios. Considering the HRV parameters, there were statistically significant differences between the five male healthy groups in terms of HRV temporal parameters and there are no significant differences in terms of HRV frequency parameters.ConclusionAs the age increases, basal Tp‐e interval increases and HRV temporal parameters decrease significantly in the male subjects aged between 30 and 79 years, but HRV frequency parameters do not change.

Concomitant Evaluation of Heart Period and QT Interval Variability Spectral Markers to Typify Cardiac Control in Humans and Rats.

The variability of heart period, measured as the time distance between two consecutive QRS complexes from the electrocardiogram (RR), was exploited to infer cardiac vagal control, while the variability of the duration of the electrical activity of the heart, measured as the time interval from Q-wave onset to T-wave end (QT), was proposed as an indirect index of cardiac sympathetic modulation. This study tests the utility of the concomitant evaluation of RR variability (RRV) and QT variability (QTV) markers in typifying cardiac autonomic control of humans under different experimental conditions and of rat groups featuring documented differences in resting sympatho-vagal balance. We considered: (i) 23 healthy young subjects in resting supine position (REST) undergoing head-up tilt at 45° (T45) and 90° (T90) followed by recovery to the supine position; (ii) 9 Wistar (WI) and 14 wild-type Groningen (WT) rats in unstressed conditions, where the WT animals were classified as non-aggressive (non-AGG, n = 9) and aggressive (AGG, n = 5) according to the resident intruder test. In humans, spectral analysis of RRV and QTV was performed over a single stationary sequence of 250 consecutive values. In rats, spectral analysis was iterated over 10-min recordings with a frame length of 250 beats with 80% overlap and the median of the distribution of the spectral markers was extracted. Over RRV and QTV we computed the power in the low frequency (LF, from 0.04 to 0.15 Hz in humans and from 0.2 to 0.75 Hz in rats) band (LFRR and LFQT) and the power in the high frequency (HF, from 0.15 to 0.5 Hz in humans and from 0.75 to 2.5 Hz in rats) band (HFRR and HFQT). In humans the HFRR power was lower during T90 and higher during recovery compared to REST, while the LFQT power was higher during T90. In rats the HFRR power was lower in WT rats compared to WI rats and the LFQT power was higher in AGG than in non-AGG animals. We concluded that RRV and QTV provide complementary information in describing the functioning of vagal and sympathetic limbs of the autonomic nervous system in humans and rats.

T-wave Area Dispersion in Coronary Artery Bypass Grafting is Indicative for Increased Risk of Adverse Events in Diabetics.

Problems in measuring the QT-dispersion are associated mostly with the inaccurate location of the T-wave end. The complications are: (i) In methodology due to various definition for Tend, (ii) In automatic measurements, due to low amplitude of T-wave, presence of U-wave and noise, and (iii) In manual measurements, due to lack of repeatability in the results, and involuntary subjectivism, when the QT-dispersion is measured by a person familiar with the ultimate goal of the study. New ECG repolarization parameter, 'T-Wave Area Dispersion' (TWAD), has been defined by Kenttä et al. 2018. Clustering ability of TWAD for prediction of risk for Sudden Cardiac Death (SCD) has been proven by the authors, working with a large database. We have measured TWAD in peri-, and postoperative ECG recordings of patients, undergoing coronary artery bypass grafting. Analysis of perioperative TWAD has shown an increased risk of adverse events in diabetics. Postoperative TWAD parameters have deteriorated proportionally in both diabetics/ non-diabetics groups indicating increased cardiac risk within the first ten postoperative days. The ability for diabetics/non-diabetics clustering of TWAD has been proven even in case of inaccurate location of the Tend. So far this is a reasonable advantage of TWAD vs. QT-dispersion in the study of ECG repolarization.

Detection of End of T-wave in Fetal ECG Using Recurrence Plots.

Automatic detection of fetal ECG features can assist in diagnosis of fetal cardiac complications and may reduce the time required for diagnosis. Detection of the end of the repolarization period wave in ECG has been proven challenging due to its low amplitude and low frequency range. The prolongation of end of T-wave is associated with sudden cardiac death, thus, methods that can accurately pinpoint it is highly desirable for early diagnosis of cardiac diseases. In this paper, a technique based on recurrence plots is developed for the detection of end of T-wave. The developed technique was tested on maternal ECG (mECG), fetal scalp ECG (fsECG) and non-invasive fetal ECG (nfECG) records. The technique was able to detect end of T-waves in all of the mECG beats, 75% of the non-invasive fECG beats (verified by simultaneously captured doppler ultrasound signals) and 78% of the fsECG beats. Detection of fECG signals were more challenging than mECG signals due to the noise and their low amplitude T-waves.

Differences in Brugada Syndrome Patients Through Ventricular Repolarization Analysis During Sleep.

Brugada syndrome (BS) is a genetic pathology that might cause sudden cardiac death (SCD) in patients with a structurally normal heart. Repolarization disorders have been postulated as a potential substrate for triggering cardiac arrhythmia in BS, that usually occur at rest or during sleep. In this paper, we have characterized ventricular repolarization markers during sleep on patients suffering from BS. To this end, standard 12-lead ECG recordings were analyzed in a population of 110 BS patients (25 symptomatic). The QT and the T-wave peak to T-wave end intervals (respectively QT and Tpe) were assessed from lead V5. The linear relationship between these markers and the instantaneous heart rate period (RR interval) are determined during each hour and for the whole sleep period. From the models obtained, corrected QT and Tpe measures were then estimated for each patient at 60 beats/min (QT60 and Tpe60) and at the mean heart rate observed during the involved time interval (QTHR and TpeHR). Results show larger values for symptomatic patients in all markers, with significant differences with respect to the asymptomatic group in the case of Tpe (Tpe60: p = 0.0012; TpeHR: p = 0.0014). Moreover, the temporal profiles of these markers reveal major differences among BS subgroups during the last 3 hours of sleep, where symptomatic patients presented increased QT60/HR (p = 0.01) and Tpe60/HR (p <; 0.001), as compared to the initial sleep hours. We conclude that BS patients present different repolarization properties according to their symptomatology, especially during the final stage of sleep.

SWITCHING FROM RAMIPRIL TO SACUBITRIL/VALSARTAN FAVORABLY ALTERS ELECTROCARDIOGRAPHIC INDICES OF VENTRICULAR REPOLARIZATION IN HEART FAILURE WITH REDUCED EJECTION FRACTION

Angiotensin receptor neprilysin inhibitor (ARNI, sacubitril/valsartan) reduces sudden death in heart failure with reduced ejection fraction (HFrEF). Corrected QT (QTc), T-wave peak to T-wave end interval (Tp-e) and Tp-e/QTc are electrocardiographic indices of ventricular repolarization heterogeneity