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Notes: Background:Spectral techniques for the analysis of signal-averaged electrocardiogram (ECG) are superior to the standard time-domain analysis in special circumstances but none of these techniques is better than the time-domain analysis in the prospective risk stratification of survivors of myocardial infarction (Ml). Aim:This study applied wavelet decomposition of signal-averaged ECGs to prospective risk stratification of Ml survivors and compared its performance with that of conventional time-domain analysis. Methods:Eligible patients were aged 〈inlineGraphic alt="leqslant R: less-than-or-eq, slant" extraInfo="nonStandardEntity" href="urn:x-wiley:1082720X:ANEC20:les" location="les.gif"/〉 75 years without atrial fibrillation, important noncardiac disease, bundle branch block, ventricular preexcitation, permanent pacemaker, or a history of cardiac surgery. Of 754 eligible patients, signal-averaged ECG recordings were available in 551. During a 2-year follow-up, 32 patients suffered from cardiac death (CM), 21 potentially arrhythmic death, 19 sudden cardiac death (SCO), 19 developed nonfatal sustained ventricular tachycardia (VT), and 5 were resuscitated from an ECG-documented ventricular fibrillation. Signal-averaged ECG indices from both analyses were compared in patients with and without different follow-up events. The positive predictive characteristics (dependency of positive predictive accuracy on sensitivity) for predicting different follow-up events based on both modes of analysis were compared at selected levels of sensitivity. Results: Time-domain signal-averaged ECGs were strongly correlated with incidence of VT (P = 0.01). Positive wavelet analysis was more strongly correlated with this event (P 〈 0.005) and with cardiac mortality (P 〈 0.05). For all events, wavelet analysis gave higher positive predictive accuracy (PPA) than the time-domain analysis, e.g., the prediction of SCD at sensitivity of 25%, wavelet and time-domain analyses gave PPA of 20.0% and 6.5%, respectively (P 〈 10-10). Conclusion:Wavelet decomposition analysis of signal-averaged ECGs is superior to the standard time-domain analysis in predicting post-MI events. In particular, this analysis identifies not only those post-MI patients who are at high risk of nonfatal sustained VT but also those who are at risk of SCD. A.N.E. 2000;5(1):20–29

Notes: Background: Although prolonged duration of the signal-averaged (SA) P wave has been proposed as a noninvasive marker of atrial arrhythmias, clinical value of atrial SAECG is limited, largely due to the difficulty with detection of the onset and offset of the high gain P wave. The aim of this study was to assess the reliability of automatic measurement of the atrial SAECG.Methods: Fifty-one healthy volunteers (30 men; 32 ± 8 years) underwent a session of 3 atrial and 3 ventricular SAECG recordings. Automatically detected onset and offset of SA QRS complex (QRStot) and SA P wave (Ptot) were subsequently-corrected by two independent observers. For ventricular SAECG, three conventional time-domain parameters were calculated. For atrial SAECG, the following five parameters were measured: Ptot, root mean square voltages of the entire Ptot (RMS-P) and of the terminal 40, 30, and 20 ms of Ptot. Relative errors of the different pairs of measurements were used to assess the interobserver and observer-computer variability. The Bland-Altman method was applied to express the agreement between measurements.Results: Although the mean interobserver relative errors were low for QRStot and Ptot (1.1% vs 1.5%), the observer-computer error was significantly higher for Ptot than for QRStot (1.7% vs 7.1%; P 〈 0.0001). For the voltage parameters, the lowest interobserver and observer-computer relative errors were found for RMS-P (6.6% vs 7.3%, P = ns). For RMS voltages of the terminal 40–20 ms of Ptot, relative errors exceeded 10%, but the interobserver error was significantly lower than the observer-computer error (P 〈 0.0001).Conclusion: Automatic detection of the SA P-wave onset and offset is unreliable and the atrial SAECG requires manual correction. Given a good interobserver agreement, such a correction is unlikely to introduce any significant observer-dependent bias. A.N.E. 2000; 5(2):133–138

Notes: Objective: Although gender specificities of various risk factors have been well documented, risk stratification after myocardial infarction has never been compared in women and men.Methods: The power of left ventricular ejection fraction, heart rate variability, and mean RR interval computed from 24-hour Holter recordings, was compared in women and men for the prediction of cardiac mortality after an acute myocardial infarction. The study population consisted of 456 patients (108 women, 348 men) aged 50–75 years.Results: During a follow-up of 3 years, there were 41 cardiac deaths (13 women vs 28 men, P = NS). The positive predictive accuracy of left ventricular ejection fraction, heart rate variability, and mean RR interval at all sensitivity levels was higher in women than in men. For a 40% sensitivity, positive predictive accuracy of left ventricular ejection fraction was 46% in women and 16% in men (P 〈 0.05), positive predictive accuracy of mean RR interval was 90% in women and 28% in men (P 〈 0.05), and positive predictive accuracy of heart rate variability was 61% in women and 43% in men (P = NS). Mean RR interval had the highest positive predictive accuracy for cardiac mortality in women, but its superiority over heart rate variability was not statistically significant. In men, heart rate variability was the strongest predictor of mortality that was significantly more powerful than mean RR interval and left ventricular ejection fraction (P 〈 0.05).Conclusion: Increased 24-hour mean heart rate is the strongest predictor of cardiac mortality in women in whom it performs significantly better than in men. While in men, heart rate variability is a significantly better predictor of postinfarction cardiac mortality than 24-hour mean heart rate, this is not the case in women.

Notes: Background: The study evaluated interobserver differences in the classification of the T-U wave repolarization pattern, and their influence on the numerical values of manual measurements of QT interval duration and dispersion in standard predischarge 12-lead ECGs recorded in survivors after acute myocardial infarction.Methods: Thirty ECGs recorded at 25 mm/s were measured by six independent observers. The observers used an adopted scheme to classify the repolarization pattern into 1 of 7 categories, based on the appearance of the T wave, and/or the presence of the U wave, and the various extent of fusion between these. In each lead with measurable QRST(U) pattern, the RR, QJ, QT-end, QT-nadir (i.e., interval between Q onset and the nadir or transition between T and U wave) and QU interval were measured, when applicable. Based on these measurements, the mean RR interval, the maximum, minimum, and mean QJ interval, QT-end and/or QT-nadir interval, and QU interval, the difference between the maximum and minimum QT interval (QT dispersion [QTD]), and the coefficient of variation of QT intervals was derived for each recording. The agreement of an individual observer with other observers in the selection of a given repolarization pattern were investigated by an agreement index, and the general reproducibility of repolarization pattern classification was evaluated by the reproducibility index. The interobserver agreement of numerical measurements was assessed by relative errors. To assess the general interobserver reproducibility of a given numerical measurement, the coefficient of variance of the values provided by all observers was computed for each ECG. Statistical comparison of these coefficients was performed using a standard sign test.Results: The results demonstrated the existence of remarkable differences in the selection of classification patterns of repolarization among the observers. More importantly, these differences were mainly related to the presence of more complex patterns of repolarization and contributed to poor interobserver reproducibility of QTD parameters in all 12 leads and in the precordial leads (relative error of 31%–35% and 34%–43%, respectively) as compared with the interobserver reproducibility of both QT and QU interval duration measurements (relative error of 3%–6%, P 〈 0.01). This observation was not explained by differences in the numerical order between QT interval duration and QTD, as the reproducibility of the QJ interval (i.e., interval of the same numerical order as QTD was significantly better (relative error of 7.5%–13%, P 〈 0.01) than that of QTD.Conclusions: Poor interobserver reproducibility of QT dispersion related to the presence of complex repolarization patterns may explain, to some extent, a spectrum of QT dispersion values reported in different clinical studies and may limit the clinical utility in this parameter.

Notes: Background: Depressed heart rate variability (HRV) is associated with increased risk for sudden cardiac death after myocardial infarction. Beta-blocker therapy reduces the risk of sudden cardiac death in patients with recent infarction. There is also evidence that beta-blockers improve HRV in postinfarction patients. In this study, we investigated whether the association between HRV and cardiac mortality in postinfarction patients is different in those who, on clinical grounds are and are not discharged on beta-blocker therapy.Methods: HRV was assessed from 24-hour ambulatory electrocardiograms before hospital discharge in 438 survivors of acute myocardial infarction. After the recordings, 147 patients were prescribed beta-blockers and 291 were discharged without beta-blocker therapy. The patients were followed for at least 2 years using cardiac death and arrhythmic death as clinical endpoints. Patients were dichotomized to depressed and normal HRV at the lowest 30 percentile.Results: Multivariate logistic regression analysis showed that HRV was a sigificant determinant of cardiac (P 〈 0.001) and arrhythmic mortality (P 〈 0.001) in patients who were not on beta-blocker therapy, whereas it was not a predictor of cardiac or arrhythmic mortality in patients who were taking beta-blockers. Beta-blocker therapy was associated with a significantly lower total cardiac mortality and arrhythmic mortality in patients with depressed HRV (P 〈 0.01 and P 〈 0.05, respectively). In patients with normal HRV, the reduction of mortality was smaller and remained nonsignificant.Conclusion: HRV was not a predictor of cardiac mortality in postinfarction patients who were prescribed beta-blockers before hospital discharge. In addition, beta-blocker therapy was associated with a lower cardiac mortality, particularly in patients with depressed HRV. Thus, depressed HRV might be considered as an additional indication for beta-blocker therapy in postinfarction patients.

Notes: Background:Wavelet representation is able to detect low amplitude patterns even if hidden within signals of much higher amplitudes. Method:A software system has been developed that implements wavelet representation of signal-averaged electrocardiograms (SAECG). In this system, wavelet analysis leads to 4 numerical parameters that characterize the content of low amplitude perturbations found within the high gain QRS complex. In three substudies, these numerical parameters were compared with the standard time-domain indices of SAECG. Populations:Normal limits were identified from recordings of 104 normal healthy volunteers (54 males, mean age 50 ± 17 years). Short-term reproducibility of the numerical indices and of abnormal findings was evaluated in a population of 85 subjects (16 healthy volunteers, 22 patients with documented ventricular tachycardia [VT] without structural heart disease, 30 patients with documented sustained postinfarction VT, and 17 survivors of acute myocardial infarction) who were each recorded three times with 5-minute periods separating individual recordings. The power of wavelet and time-domain analyses in distinguishing patients with and without sustained VT after myocardial infarction was assessed using recordings of 53 patients with postinfarction VT and of 53 age, sex, and infarct site matched patients without a history of arrhythmic complications after infarction. Results:The studies have shown that (a) the indices of wavelet analysis are more reproducible than the time-domain indices, (b) the distinction between patients with and without VT after myocardial infarction is similarly powerful by wavelet and time-domain analyses, and the association of the positive SAECG analysis with postinfarction VT is highly significant with both analyses (P = 3.94 × 10–14 for wavelet analysis and 2.55 × 10−9 for time-domain analysis), the indices of wavelet analysis differ significantly between normals and patients with an uncomplicated history of myocardial infarction (P = 0.02–0.005), while time-domain indices do not (all parameters NS), (d) in contrast to the time-domain analysis, wavelet analysis was similarly powerful in identifying VT patients with anterior and inferior infarction (P = 1.4 × 10−9, n = 30, and P = 2.0 × 10−15, n = 23, respectively). Conclusion:Wavelet analysis is a highly reproducible method for SAECG processing which (a) is as powerful as the time-domain analysis for the identification of ischemic VT patients, (b) compared to the time-domain analysis, is not dependent on infarct site, and is able to distinguish postmyocardial infarction patients without VT from healthy subjects. A.N.E. 2000,5(1):4–19

Notes: Background:Abnormalities in the adaptation of the QT interval to changes in the RR interval may facilitate the development of ventricular arrhythmias. Methods:This study sought to evaluate the dynamic relation between the QT and RR intervals in patients after acute myocardial infarction. The study population consisted of 14 patients after myocardial infarction (age 60 ± 7 years, 12 men) who died suddenly (SCD victims) within 1 year after the myocardial infarction and 14 pair-matched age, sex, left ventricular ejection fraction, infarct site, thrombolytic therapy) patients who remained event-free after myocardial infarction (Ml survivors) for at least 3 years. Fourteen normal subjects were studied as controls (age 55 ± 9 years, 11 men). QT and RR intervals were measured on a beat-to-beat basis automatically with a visual control from 24-hour ambulatory ECGs using Reynolds Pathfinder 700. Mean hourly values of the QT/RR slope (QT =α+βRR) and corrected QT interval at 1000 ms of RR interval (QT1s) were derived for each subject using an inhouse program (QT1s=α+1000β). The dynamics of the QT/RR slope and QT1s were assessed on the basis of hourly mean values. The circadian rhythm of ventricular repolarization (QT1s and QT/RR slope) was examined by harmonic regression analysis. Results:There was a trend towards a significant difference in 24-hour mean value of QT1s between study groups (408 ± 26 ms vs 381 ± 43 ms and 386 ± 22 ms, P = 0.06), and a significant difference was found between SCD victims and normal subjects (408 ± 26 vs 386 ± 22 ms, P = 0.02). The QT1s differed significantly between study groups (P = 0.038) only during the day time (09:00–19:00 hour), when QT1s was significantly longer in SCD victims than in normal subjects (409 ± 33 vs 380 ± 27 ms, P = 0.02) and tended to be longer than in Ml survivors (409 ± 33 vs 379 ± 42 ms, P = 0.08). The 24-hour mean value of QT/RR slope was significantly different between study groups (P = 0.04), with a significantly steeper slope in SCD victims than in normal subjects (0.15 ± 0.07 vs 0.09 ± 0.02, P = 0.008). During day time, the QT/RR slope differed significantly between study groups (P = 0.04), while the difference was less marked at night (P = 0.08). The slope was significantly steeper in SCD victims than in normal subjects during both day and night (P 〈 0.05). A marked circadian variation of QT1s was observed in normal subjects, which was blunted in Ml survivors and SCD victims. Conclusions:Abnormal repolarization behaviors, characterized by longer QT1s and impaired adaptation of QT to variations in RR intervals, were found in SCD victims. Hence, lethal ventricular tachyarrhythmias might be provoked by the altered repolarization dynamics in patients after myocardial infarction. A.N.E. 1999;4(3):286–294

Notes: Background: This study examined the possible role of atrial ectopics and short runs of atrial tachycardia in the initiation of episodes of paroxysmal atrial fibrillation (PAF).Methods: Holter recordings from patients participating in pharmacotherapy trials for the prevention of PAF were examined. Treatment comprised placebo, digoxin, disopyramide, or atenolol. The frequency of atrial ectopic beats during each 30 seconds over the 5 minutes prior to PAF and whether this was also associated with atrial tachycardia (3 or more ectopics in succession) was calculated.Results: The mean number of ectopics was 4.1 in the final minute, but patients receiving disopyramide or atenolol had significantly more ectopics than those on placebo (P 〉 0.05 for both). Those on digoxin had a similar number of ectopics to placebo patients. There was no relationship between heart rate at PAF onset and ectopic frequency, nor any association between the presence of one or more episodes of atrial tachycardia and ectopic frequency.Conclusion: Atrial ectopics increase in frequency prior to PAF onset, and this study suggests that antiarrhythmic therapy may increase the number of ectopics required to initiate PAF.

Notes: Background: The mean ventricular rate is known to be, on average, higher during episodes of paroxysmal atrial fibrillation (AF) than during the sinus rhythm. The data on other statistical parameters of ventricular periods are less known, especially in the respect of atrial premature beats, which are frequent in paroxysmal AF patients while in sinus rhythm.Methods: This study investigated distinction between paroxysmal AF and sinus rhythm based on statistical parameters of 60-second segments of RR interval series from paroxysmal AF and sinus rhythm episodes. The data of the study were taken from 54 long-term Holter recordings of 31 paroxysmal AF patients. For each segment, the mean, standard deviation, skewness, and kurtosis of RR intervals were computed. The values obtained from segments of paroxysmal AF and sinus rhythm were used in attempts to distinguish both rhythms. The distinction was evaluated using: (1) statistical comparison of each parameter in each tape; and (2) receiver operator characteristics (ROCs) (dependency of specificity on sensitivity) computed in each tape for each individual parameter as well as for multivariate combinations of all four parameters.Results: The comparison found the mean RR to be the most powerful discriminator of all statistical parameters used in the distinction of paroxysmal AF and sinus rhythm. The standard deviation of RR intervals differentiated significantly the segments of sinus rhythm and paroxysmal AF in pool data. However, when applied to individual Holter recordings, the distinction between paroxysmal AF and sinus rhythm based on standard deviation of RR intervals (assessed with ROC) was the worst among all investigated parameters.Conclusions: The study suggests that an automatic distinction between paroxysmal AF and sinus rhythm episodes is highly problematic, mainly because of the frequent atrial premature beats that pollute sinus rhythm episodes in patients with paroxysmal AF.

Notes: QT Dispersion and Repolarization Heterogeneity. Introduction: QT dispersion (QTd, range of QT intervals in 12 ECG leads) is thought to reflect spatial heterogeneity of ventricular refractoriness. However, QTd may be largely due to projections of the repolarization dipole rather than “nondipolar” signals. Methods and Results: Seventy-eight normal subjects (47 ± 16 years, 23 women), 68 hypertrophic cardiomyopathy patients (HCM; 38 ± 15 years. 21 women), 72 dilated cardiomyopathy patients (DCM; 48 ± 15 years, 29 women), and 81 survivors of acute myocardial infarction (AMI; 63 ± 12 years, 20 women) had digital 12-lead resting supine ECGs recorded (10 ECGs recorded in each subject and results averaged). In each ECG lead, QT interval was measured under operator review by QT Guard (GE Marquette) to obtain QTd. QTd was expressed as the range, standard deviation, and highest-to-lowest quartile difference of QT interval in all measurable leads. Singular value decomposition transferred ECGs into a minimum dimensional time orthogonal space. The first three components represented the ECG dipole; other components represented nondipolar signals. The power of the T wave nondipolar within the total components was computed to measure spatial repolarization heterogeneity (relative T wave residuum, TWR). OTd was 33.6 ± 18.3, 47.0 ± 19.3, 34.8 ± 21.2, and 57.5 ± 25.3 msec in normals, HCM, CM, and AMI, respectively (normals vs DCM: NS, other P 〈 0.009). TWR was 0.029%± 0.031%, 0.067%± 0.067%, 0.112%± 0.154%, and 0.186%± 0.308% in normals, HCM, DCM, and AMI (HCM vs DCM: NS. other P 〈 0.006), The correlations between QTd and TWR were r = -0.0446, 0.2805, -0.1531, and 0.0771 (P = 0.03 for HCM, other NS) in normals, HCM, DCM, and AMI, respectively. Conclusion: Spatial heterogeneity of ventricular repolarization exists and is measurable in 12-lead resting ECGs. It differs between different clinical groups, but the so-called QT dispersion is unrelated to it.