Variations in the dispersion of ventricular repolarization can be quantified by T-wave time-warping based index,$d_w$. However, the early phase of the T-wave can be affected by ST-segment changes during ischemia. We hypothesized that restricting$d_{w}$to the T-wave peak-to-end ($T_{pe}$) would circumvent this limitation while still quantifying variations in repolarization dispersion. A total of 101 ECG recordings from patients undergoing coronary occlusion, together with their control recordings, were analyzed. A series of$d_{w}$values was calculated by quantifying the$T_{pe}$morphological variations between the T-waves at different occlusion stages and a baseline T-wave. We introduced a normalized version of$d_w$,$\mathcal {R}_d$, reflecting variations of$d_w$during occlusion relative to control recordings ($\mathcal{R}_d = 1$corresponds to the same level of variation). The$d_w$series followed a gradually increasing trend with occlusion time, reaching median [range]$\mathcal {R}_d$values of 9.44 [1.01, 80.74] at the occlusion end.$\mathcal {R}_d$at occlusion end was significantly higher than threshold values of 1, 2, 5, and 10 in 94.1%, 85.11%, 64.4% and 48.5% of patients, respectively. The spatial lead-wise analysis of$d_w$showed distinct distributions depending on the occluded artery, suggesting a relation with the ischemia location. The relative variation$\mathcal {R}$with ischemia of index$d_w$(9.4) is greater than that of the T-wave amplitude (7.7),$T_{pe}$interval (2.7) and T-wave width (3.0). In conclusion,$d_w$tracks ischemic-induced variations in repolarization dispersion in a more robust manner than classical indexes, avoiding the impact of ST segment elevation/depression or early T-wave distortions, thus warranting further clinical studies.

OBJECTIVE: T-wave alternans (TWA) manifests as beat-to-beat fluctuations of T-wave morphology on the electrocardiogram (ECG), with physiological bases not fully understood. Using a biophysical model of the ECG, we demonstrate and give explicit relations that TWA depends on the i) spatial covariance between myocytes' repolarization time and alternans; and ii) global alternans (common to every myocyte). METHODS: We quantified the spatial covariance and global alternans by means of two new metrics, R index and δ, respectively. They were validated on both synthetic and real signals. Computerized simulations were generated using a biophysical model linking the action potentials with the surface ECG. Then, the metrics were computed in STAFF-III dataset, containing ECGs from patients who underwent coronary angioplasty with prolonged balloon inflations, and the time courses of the metrics were analyzed together with TWA measured on the surface ECG. RESULTS: The metrics properly estimated the spatial covariance and global alternans in the synthetic data. In the STAFF-III dataset, the R index progressively increased from baseline to the fourth minute of inflation (median ∆R=0.81 ms; p 0.05), whereas δ was mostly unaltered during the intervention ( δ=0 ms). CONCLUSION: We reported, for the first time, that TWA is significantly driven by the myocyte's spatial covariance between their repolarization times and alternans, and not by global alternans, when TWA is generated by regional ischemia. SIGNIFICANCE: The metrics may reveal new complementary insights into the mechanisms underlying TWA.

Objective: Atrial fibrillation (AF) rhythm gives rise to an irregular response in ventricular activity, preventing the use of standard ECG-derived risk markers based on ventricular repolarization heterogeneity under this particular condition. In this study, we proposed new indices to quantify repolarization variations in AF patients, assessing their stratification performance in a chronic heart failure population with AF. Methods: We developed a method based on a selective bin averaging technique. Consecutive beats preceded by a similar RR interval were selected, from which the average variation within the ST-T complex for each RR range was computed. We proposed two sets of indices: 1) the 2-beat index of ventricular repolarization variation, (IV2), computed from pairs of stable consecutive beats; and 2) the 3-beat indices of ventricular repolarization variation, computed in triplets of stable consecutive beats (IV3). Results: These indices showed a significant association with sudden cardiac death (SCD) outcome in the study population. In addition, risk assessment based on the combination of the proposed indices improved stratification performance compared to their individual potential. Conclusion: Patients with enhanced ventricular repolarization variation computed in terms of the proposed indices were successfully associated to a higher SCD incidence in our study population, evidencing their prognostic value. Significance: using a simple ambulatory ECG recording, it is possible to stratify AF patients at risk of SCD, which may help cardiologists in adopting most effective therapeutic strategies, with a positive impact in both the patient and healthcare systems.

Instabilities in ventricular repolarization have been documented to be tightly linked to arrhythmia vulnerability. Translation of the information contained in the repolarization phase of the electrocardiogram (ECG) into valuable clinical decision-making tools remains challenging. This work aims at providing an overview of the last advances in the proposal and quantification of ECG-derived indices that describe repolarization properties and whose alterations are related with threatening arrhythmogenic conditions. A review of the state of the art is provided, spanning from the electrophysiological basis of ventricular repolarization to its characterization on the surface ECG through a set of temporal and spatial risk markers.

In this paper, we studied the improvement in heartbeat classification achieved by including information from multilead ECG recordings in a previously developed and validated classification model. This model includes features from the RR interval series and morphology descriptors for each lead calculated from the wavelet transform. The experiments were carried out in the INCART database, available in Physionet, and the generalization was corroborated in private and public databases. In all databases, the AAMI recommendations for class labeling and results presentation were followed. Different strategies to integrate the additional information available in the 12-leads were studied. The best performing strategy consisted in performing principal component analysis to the wavelet transform of the available ECG leads. The performance indices obtained for normal beats were sensitivity (S) 98%, positive predictive value (P(+)) 93%; for supraventricular beats, (S) 86%, (P(+)) 91%; and for ventricular beats (S) 90%, (P(+)) 90%. The generalization capability of the chosen strategy was confirmed by applying the classifier to other databases with different number of leads with comparable results. In conclusion, the performance of the reference two-lead classifier was improved by taking into account additional information from the 12-leads.