Effect of Noise on T-Wave Alternans Measurement in Ambulatory ECGs Using Modified Moving Average versus Spectral Method

Sudden death and its predictors in myocardial infarction survivors in an Indian population

Objective

This study was conducted to assess the incidence of sudden cardiac death (SCD) in post myocardial infarction patients and to determine the predictive value of various risk markers in identifying cardiac mortality and SCD.

Methods

Left ventricular function, arrhythmias on Holter and microvolt T wave alternans (MTWA) were assessed in patients with prior myocardial infarction and ejection fraction ≤ 40%. The primary outcome was a composite of cardiac death and resuscitated cardiac arrest during follow up. Secondary outcomes included total mortality and SCD.

Results

Fifty-eight patients were included in the study. Eight patients (15.5%) died during a mean follow-up of 22.3 ± 6.6 months. Seven of them (12.1%) had SCD. Among the various risk markers studied, left ventricular ejection fraction (LVEF) ≤ 30% (Hazard ratio 5.6, 95% CI 1.39 to 23) and non-sustained ventricular tachycardia (NSVT) in holter (5.7, 95% CI 1.14 to 29) were significantly associated with the primary outcome in multivariate analysis. Other measures, including QRS width, heart rate variability, heart rate turbulence and MTWA showed no association.

Conclusions

Among patients with prior myocardial infarction and reduced left ventricular function, the rate of cardiac death was substantial, with most of these being sudden cardiac death. Both LVEF ≤30% and NSVT were associated with cardiac death whereas only LVEF predicted SCD. Other parameters did not appear useful for prediction of events in these patients. These findings have implications for decision making for the use of implantable cardioverter defibrillators for primary prevention in these patients.

Reference values for a novel ambulatory-based frequency domain T-wave alternans in subjects without structural heart disease

BACKGROUND

Conventional frequency domain T wave alternans (FD-TWA) is a noninvasive risk stratification marker for identifying arrhythmic sudden cardiac death, but the conventional FD-TWA device that was considered the gold standard device has been discontinued commercially. Recently, a newly developed ambulatory electrocardiogram (AECG) device that can detect FD-TWA continuously for 24 hours is available in clinical settings. However, information on the normal values using the novel AECG-based frequency domain TWA (FD-TWA) is lacking.

METHODS

FD-TWA for AECG was examined in 312 subjects without heart disease (Sb-wHD) (range 20-89 years, 146 men) and 30 heart disease patients (HD-P) (mean age 57±17 years, 24 men). The maximum FD-TWA amplitude over 24 hours was measured with manual editing. The upper limit of local noise levels for measurement of FD-TWA was set to both <10 μV and <20 μV (acceptable noise level <10 μV and <20 μV).

RESULTS

The reference values (95th percentiles) of FD-TWA in Sb-wHD were 19.9 μV for the acceptable noise level <10 μV and 23.6 μV for the acceptable noise level <20 μV. The 75th percentile of FD-TWA amplitude in HD-P was 19.5 µV at an acceptable noise level <10 µV and 21.5 µV at an acceptable noise level <20 µV. FD-TWA amplitude without heart disease was significantly affected by heart rate when the maximum FD-TWA was measured (β = 0.274 p < 0.001 for the acceptable noise level <10 μV; β = 0.263, p < 0.001 for the acceptable noise level <20 μV) and age (β = 0.204, p = 0.004 for the acceptable noise level <10 μV; β = 0.149, p = 0.034 for the acceptable noise level <20 μV).

CONCLUSIONS

In the present study, the reference values for a novel FD-TWA in Sb-wHD and the distribution of TWA values in HD-P were established. In future research, the cut-off values of FD-TWA in HD-P will need to be examined.

Diurnal variation of frequency domain T-wave alternans on 24-hour ambulatory electrocardiogram in subjects without heart disease: Significant effect of autonomic nervous activity of the heart

BACKGROUND

T-wave alternans (TWA) is a risk stratification predictor for sudden cardiac death. However, little is known about the diurnal variation of TWA. Whether TWA are affected by heart rate (HR) or cardiac autonomic nervous activity in the subjects without significant structural heart disease in daily life is not fully understood. Thus, this study was aimed to clarify these issues.

METHODS

Frequency domain (FD)-TWA analysis was conducted in 47 subjects without significant structural heart disease using 24-hr ambulatory electrocardiogram (AECG). Measurement of heart rate variability (HRV) was performed in order to evaluate the autonomic activity of the heart. The maximum FD-TWA value in each period was measured four times per day (A, 00:00-6:00 hr; B, 06:00-12:00 hr; C, 12:00-18:00 hr; D, 18:00-24:00 hr). Correlations between FD-TWA and either HR or HRV parameters (LF/HF, LFnu, HFnu, SDNN, CVNN, pNN50) were analyzed in each period (A-D).

RESULTS

There was diurnal variation of FD-TWA (median, inter-quartile range [IQR]: A, 8.2 [6.5, 10.6] μV; B, 10.1 [8.4, 15.0] μV; C, 17.6 [12.3, 25.0] μV: D, 11.9 [9.1, 19.9] μV; p < 0.0001). Maximum FD-TWA had positive correlations with HR and LF/HF (HR, r = 0.496, p < 0.0001; LF/HF, r = 0.414, p = 0.004), while FD-TWA had a negative correlation with HFnu (r = -0.291, p = 0.048). On multiple linear regression analysis, HR had an independent effect on log FD-TWA amplitude (β = 0.461, p = 0.001).

CONCLUSIONS

FD-TWA has marked diurnal variation in the daily life of the subjects without significant structural heart disease. This variation could be more strongly affected by HR than the HRV indices.

Risk Stratification for Ventricular Tachyarrhythmias by Ambulatory Electrocardiogram-Based Frequency Domain T-Wave Alternans

BACKGROUND

Ambulatory electrocardiogram (ECG)-based T-wave alternans (TWA) quantified by the modified moving average method (MMA) can be used to identify patients at risk for sudden cardiac death. However, there is no information available on ambulatory ECG-based TWA as quantified by the frequency domain (FD) method to identify patients with an implantable cardioverter defibrillator (ICD) who are at high risk for ventricular tachyarrhythmias. Further, there are few data regarding the comparison of clinical utility of FD-TWA with MMA-TWA, heart rate variability (HRV), and heart rate turbulence (HRT).

METHODS AND RESULTS

In 41 patients with ICD, of whom 14 patients had a past history of at least one appropriate ICD discharge, FD-TWA, MMA-TWA, HRV, and HRT were analyzed from 24-hour Holter ECG monitoring recordings. Only positive results of FD-TWA and abnormal HRV (standard deviation of all normal-to-normal intervals ≤111 ms) were significantly more frequently observed in patients with than without appropriate ICD discharge. Patients with FD-TWA positive had a significantly higher risk of appropriate ICD discharge than those with FD-TWA negative (50% vs 16%; odds ratio, 5.3 [95% confidence interval, 1.2-23.7], P = 0.02). When FD-TWA and HRV were combined, the specificity (93% vs 59%, P = 0.003) and predictive accuracy (83% vs 66%, P = 0.07) for the identification of patients with appropriate ICD discharge were greater than those for FD-TWA only.

CONCLUSION

The ambulatory ECG-based FD-TWA might be useful to detect patients with ICD who are at high risk for ventricular tachyarrhythmias, and the combination of FD-TWA and HRV might improve the ability to detect such high-risk patients.

A nonparametric surrogate-based test of significance for T-wave alternans detection

We present a nonparametric adaptive surrogate test that allows for the differentiation of statistically significant T-wave alternans (TWA) from alternating patterns that can be solely explained by the statistics of noise. The proposed test is based on estimating the distribution of noise-induced alternating patterns in a beat sequence from a set of surrogate data derived from repeated reshuffling of the original beat sequence. Thus, in assessing the significance of the observed alternating patterns in the data, no assumptions are made about the underlying noise distribution. In addition, since the distribution of noise-induced alternans magnitudes is calculated separately for each sequence of beats within the analysis window, the method is robust to data nonstationarities in both noise and TWA. The proposed surrogate method for rejecting noise was compared to the standard noise-rejection methods used with the spectral method (SM) and the modified moving average (MMA) techniques. Using a previously described realistic multilead model of TWA and real physiological noise, we demonstrate the proposed approach that reduces false TWA detections while maintaining a lower missed TWA detection, compared with all the other methods tested. A simple averaging-based TWA estimation algorithm was coupled with the surrogate significance testing and was evaluated on three public databases: the Normal Sinus Rhythm Database, the Chronic Heart Failure Database, and the Sudden Cardiac Death Database. Differences in TWA amplitudes between each database were evaluated at matched heart rate (HR) intervals from 40 to 120 beats per minute (BPM). Using the two-sample Kolmogorov-Smirnov test, we found that significant differences in TWA levels exist between each patient group at all decades of HRs. The most-marked difference was generally found at higher HRs, and the new technique resulted in a larger margin of separability between patient populations than when the SM or MMA were applied to the same data.

T wave alternans evaluation using adaptive time-frequency signal analysis and non-negative matrix factorization

Each year 400,000 North Americans die from sudden cardiac death (SCD). Identifying those patients at risk of SCD remains a formidable challenge. T wave alternans (TWA) evaluation is emerging as an important tool to risk stratify patients with heart diseases. TWA is a heart rate dependent phenomenon that manifests on the surface electrocardiogram (ECG) as a change in the shape or amplitude of the T wave every second heart beat. The presence of large magnitude TWA often presages lethal ventricular arrhythmias. Because the TWA signal is typically in the microvolt range, accurate detection algorithms are required to control for confounding noise and changing physiological conditions (i.e. data nonstationarity). In this study, we address the limitations of two common TWA estimation methods, spectral method (SM) and modified moving average (MMA). To overcome their limitations, we propose a modified TWA quantification framework, called Adaptive SM, that uses non-linear time-frequency distribution (TFD). In order to increase the robustness of TWA detection in ambulatory ECGs, we also propose a new technique, called non-negative matrix factorization (NMF)-Adaptive SM. We present the analytical background of these methods, and evaluate their accuracy in detecting synthetic TWA signal in simulated and real-world ambulatory ECG recordings under conditions of noise and data non-stationarity. The results of the numerical simulations support the effectiveness of the proposed approaches for TWA analysis, which may ultimately improve SCD risk assessment.