GDTF: Generalized Detection Theoretic Framework for T-Wave Alternans Analysis

T-wave alternans (TWA) is an indicator of cardiac instability and is associated with life-threatening ventricular arrhythmias. Detection of TWA in the adult has been widely investigated and is used routinely for cardiac risk assessment. Detection of TWA in the fetus, however, is much more difficult due to the low amplitude and variable configuration of the signal, the presence of strong interferences, and the brevity of fetal TWA episodes. In this paper, we present a statistical detector based on the generalized likelihood ratio test that is designed for detection of TWA in the fetus. The performance of the detector is evaluated by constructing receiver-operator characteristic curves, using simulated data and real data from subjects with macroscopic TWA. The detector is capable of detecting TWA episodes as brief as 20 beats. The detection performance is improved significantly by modeling the fetal T-wave as a low-rank, low bandwidth signal, and using maximum likelihood estimation to estimate the model parameters. This approach enables all of the data to be used to estimate the noise statistics, providing highly effective suppression of interference, including maternal interference. The method is suitable for routine use because it can be applied to raw, unprocessed recordings, allowing automated analysis of extended fetal magnetocardiography recordings.

Automatic detection of microvolt T-wave alternans in Holter recordings: Effect of baseline wandering

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.

More than 80% of emergency patients succumb to cardiac related including Sudden Cardiac Deaths (SCD) in Pakistan whereas the global deaths due to SCD are 30–40% of total deaths. T-wave alternans refer to variation in amplitude, phase or wave shape in alternating T waves in ECG signals and has been linked with the SCD and ventricular arrhythmia. TWA detection is still a challenge as in many cases amplitude of TWA is well below indispensable process noises. Empirical mode decomposition (EMD) is a empirical signal processing technique employed as noise removal tool for nonlinear and non-stationary TWA signals. Recently very matured and developed least square techniques have been employed for the detection of TWA, which were previously not explored as considered limited practicability. Least square works in time domain and better for the non-stationary TWA signals that gives the technique an edge over the other techniques that assume stationary nature of the signals. Least square with higher order polynomial curve fitting has been recently explored as a promising TWA estimation technique for risk stratification of SCD. In this paper we are presenting a novel EMD based least square T-wave alternans estimation scheme which outperforms estimation in 10–40 dB Gaussian noise. The performance comparison of the technique with bench mark modified moving average method (MMAM) for TWA estimation confirms the enhanced performance for the various practical scenarios of TWA.

Each year 400,000 North Americans die from sudden cardiac death (SCD). T- wave alternans (TWA) refers to an alternating pattern in the T-wave portion of the surface electrocardiogram (ECG) and has been shown as a risk stratifier for SCD. These subtle changes in the T-waves are in the micro-volt scale and ambulatory ECG recordings usually contain biological noise. Also, data non-stationarity owing to heart rate variability and the amplitude variability in TWA magnitude can limit the accuracy of the detection techniques. This necessitates the need for robust detection algorithms for processing such non-stationary data. In this thesis, we have proposed an Empirical Mode Decomposition (EMD) based scheme combined with the Instantaneous Frequency (IF). EMD decomposes the signal into several monocomponent signals called Intrinsic Mode Functions (IMFs). IF extracted from these IMFs provides an accurate estimate of time varying frequency components and hence can aid during characterization of TWAs. In order to validate the performance of the proposed detection technique, the feature vectors extracted from the IMFs were fed to a linear discriminant analysis (LDA) classifier. The performance assessment was carried out using two datasets: (a) Synthetic TWAs: 72 signals obtained from publicly accessible Physionet database and (b) TWAs from patients: 55 ambulatory ECG signals obtained from the Toronto General Hospital. Using an unbiased leave-one-out cross validation strategy, maximum overall classification accuracies of 86.1% and 81.8% were achieved for TWA detection from synthetic and ambulatory ECG recordings respectively. In addition, the usability of the proposed technique has been investigated to assess its suitability for addressing another cardiovascular problem stroke. Atrial Fibrillation (AF) has been identified as a risk factor to increase the chances of stroke. The most common method in studying the complex AF electrograms is to employ dominant frequency (DF) analysis; however, due to signal non-stationarity DF does not always provide the best estimate of the atrial activation rate. As a result, analyzing the electrograms via EMD and IF has been investigated as the second contribution of this work.

195 Feasibility of measurement of Endocardial T Wave Alternans prior to Onset of Ventricular Arrhythmias in ICDs (ETWAS study)

<p><strong>Background</strong></p> <p>T wave alternans (TWA) is an electrocardiographic marker of heightened sudden death risk from ventricular tachyarrhythmias in patients with cardiomyopathy. TWA is evaluated from the 12-lead electrocardiogram, Frank lead, or Holter lead recordings, however these clinical lead configurations will not record TWA from adjacent regions of the body torso.</p> <p><strong>Objective</strong></p> <p>We tested the hypothesis that changing heart rate or ventricular activation may alter the body surface distribution of TWA such that the clinical ECG leads fail to detect TWA in some patients; thereby producing a false-negative test.</p> <p><strong>Methods </strong></p> <p>In 28 cardiomyopathy patients (left ventricular ejection fraction 28±6%), 114 unipolar electrograms were recorded across the body torso during incremental atrial pacing, followed by atrioventricular pacing at 100, 110 and 120bpm. TWA was measured from each unipolar electrogram using the spectral method. A clinically positive TWA test was defined as TWA magnitude (Valt) ≥1.9 uV with k ≥3 at ≤ 110bpm.</p> <p><strong>Results </strong></p> <p>Maximum V<sub>alt</sub> (TWA<sub>max</sub>) was greater from the body torso than clinical leads during atrial (p<0.005) and atrioventricular pacing (p<0.005). TWA<sub>max</sub> was most prevalent in the right lower chest with atrial pacing 100 bpm and shifted to the left lower chest at 120 bpm. TWA<sub>max</sub> was most prevalent in left lower chest with atrioventricular pacing at 100 bpm and shifted to the left upper chest at 120 bpm. Using the body torso as a gold standard, the false-negative rate for clinically positive TWA with clinical leads was 21% during atrial and 11% duringatrioventricular pacing. Due to TWA signal migration outside the clinical leads, clinically positive TWA became false-negative when pacing mode was switched (atrial->atrioventricular pacing) in 21% of patients.</p> <p><strong>Conclusions</strong></p> <p>The body surface distribution of TWA is modulated by heart rate and the sequence of ventricular activation in patients with cardiomyopathy, which can give rise to modest false-negative TWA signal detection using standard clinical leads</p> <p><br></p>

The ECG signal, has attracted more attention due to its importance in diagnosing conditions. Now days T-wave alternans (TWA) has been used to forecast Sudden Cardiac death (SCD). TWA is the beat-to-beat alterations changing in the T-wave amplitude. A new method is used in this paper to detect these alternans waves more exactly and quickly with heart rate calculation by RR-interval in ECG. This algorithm is implemented using MATLAB tools by correlation method. The database used in this work is MIT-BIH. The work is carried out on more than 25 cases of this database.

The analysis of t wave alternans (TWA) was introduced to identify patients with an increased risk of ventricular tachyarrhythmias. The inducibility of ventricular tachyarrhythmias and the spontaneous arrhythmic events are correlated with a positive TWA in patients with a reduced left ventricular ejection fraction and survived myocardial infarction. In contrast, this study is the first to investigate the correlation of a survived sudden cardiac death and TWA in patients without coronary heart disease and only slightly decreased left ventricular function. Sixty patients were included in the study. The TWA analysis was performed using the Cambridge Heart system (CH2000). Patients were sitting on a bicycle ergometer and exercised with a gradual increase of workload to maintain a heart rate of at least 105 beats/min. The exercise test was stopped after recording 254 consecutive low noise level heart beats. The electrocardiographic signals were digitally processed using a spectral analysis method. The magnitude of TWA was measured at a frequency of 0.5 cycles/beat. A TWA was defined as positive if the ratio between TWA and noise level was > 3.0 and the amplitude of the TWA was > 1.8 microV. Twelve (20%) of the included 60 patients showed a positive TWA. The sensitivity concerning a previous arrhythmic event amounted to 65%, the specificity up to 98%, respectively. The alternans ratio was significantly higher in patients with a previous event (30.3 +/- 53.2 vs 2.9 +/- 5.9, P < 0.001) and cumulative alternans voltage (4.67 +/- 3.55 vs 1.75 +/- 1.88 microV, P < 0.001). In 19 patients, invasively investigated by an electrophysiological study, a significant correlation between inducibility of tachyarrhythmias and a positive TWA result was found (Spearman R = 0.51, P = 0.01). In conclusion, the TWA analysis seems to identify patients with nonischemic cardiomyopathy who are at an increased risk of ventricular tachyarrhythmias.

Non-linear trend estimation of cardiac repolarization using wavelet thresholding for improved T-wave alternans analysis

Microvolt-level T-wave alternans (TWA) assessed by spectral method during an exercise stress test has been widely studied for risk stratification. Several studies have documented the association of a positive TWA with total mortality and arrhythmic events. Nevertheless, the need to achieve an elevated and stabilized heart rate resulting in a considerable proportion of indeterminate test results constitutes one of the main limitations of this method. It is well recognized that arrhythmic events may be triggered not only by physical but also by mental stress and are not necessarily associated with exercise. Detection of TWA in ambulatory electrocardiogram recordings during daily activities might be a valuable option in risk stratification. This review describes the modified moving average (MMA) technique for detection of TWA and summarizes the results of clinical studies on the prognostic value of MMA-TWA. So far, MMA-TWA has been studied in over 5000 patients including those evaluated during exercise as well as during daily activities with ambulatory ECG recordings. The results of these studies indicate that increased MMA-TWA is associated with higher risk of cardiac mortality and arrhythmic events.

B-PO03-162 MICROVOLT QRS ALTERNANS WITHOUT MICROVOLT T WAVE ALTERNANS IN HYPERTROPHIC CARDIOMYOPATHY: A NOVEL RISK MARKER OF LATE VENTRICULAR ARRHYTHMIAS

HomeCirculationVol. 115, No. 1Letter by Selvaraj and Chauhan Regarding Article, “Upsurge in T-Wave Alternans and Nonalternating Repolarization Instability Precedes Spontaneous Initiation of Ventricular Tachyarrhythmias in Humans” Free AccessLetterPDF/EPUBAboutView PDFView EPUBSections ToolsAdd to favoritesDownload citationsTrack citationsPermissions ShareShare onFacebookTwitterLinked InMendeleyReddit Jump toFree AccessLetterPDF/EPUBLetter by Selvaraj and Chauhan Regarding Article, “Upsurge in T-Wave Alternans and Nonalternating Repolarization Instability Precedes Spontaneous Initiation of Ventricular Tachyarrhythmias in Humans” Raja J. Selvaraj and Vijay S. Chauhan Raja J. SelvarajRaja J. Selvaraj Division of Cardiology, University Health Network, Toronto, Canada Search for more papers by this author and Vijay S. ChauhanVijay S. Chauhan Division of Cardiology, University Health Network, Toronto, Canada Search for more papers by this author Originally published2 Jan 2007https://doi.org/10.1161/CIRCULATIONAHA.106.650952Circulation. 2007;115:e9To the Editor:We read with interest the article by Shusterman et al1 concerning repolarization dynamics preceding spontaneous initiation of ventricular tachyarrhythmias in humans. Two related time-domain methods measured a larger magnitude of “alternans” at 30 minutes compared with 60 to 120 minutes preceding arrhythmia onset. Spectral analysis of T-wave amplitude showed a nonuniform increase in power in all frequency ranges. The authors interpret these findings as a surge in alternans and nonalternating repolarization complexity preceding the arrhythmia.An increase in sympathetic activity preceding the onset of ventricular arrhythmia was reported by the authors in the same population.2 This can lead to increased noise in the ECG recordings from perspiration and an increase in motion and respiratory artifact.3The modified moving average and, to a larger extent, the intrabeat average measurements are affected by noise that cannot be completely removed by preprocessing.3 Using these time-domain methods, the authors have previously emphasized the need to measure surrogate alternans in the isoelectric T end to P onset segment to show that an increase in T-wave alternans magnitude is not artifactually attributable to noise.3 It is unclear why the authors did not use a similar analysis of surrogate alternans in the present study1 to ensure that noise was not a confounder.Using the spectral method, white noise can spuriously increase the spectral power in the alternans frequency, and subtraction of the spectral power in a “noise band” is required to measure true alternans.4 Therefore, the increase in spectral power seen in all frequency ranges could be explained by an increase in noise rather than an increase in “nonalternating repolarization complexity.” The possibility of subharmonics influencing the measured power in the alternans range is another important confounder, particularly respiratory subharmonics (0.15 to 0.25 cpb), which increase in spectral power before the tachyarrhythmia. The authors feel the contribution of respiratory subharmonics is improbable because the increase in T-wave alternans magnitude has been confirmed by 3 independent methods. However, the concordant findings do not exclude the confounding effects of subharmonics, because these methods are all inherently influenced by subharmonics.3 Although the increase in alternans may not be accounted for entirely by respiratory subharmonics, this is likely an important contribution and is not unexpected, because respiratory excursions that influence T-wave amplitude can increase during sympathetic surges preceding tachyarrhythmia. Thus, we feel that white noise and respiratory oscillations may significantly contribute to the apparent increase in alternating and nonalternating repolarization instability.DisclosuresNone.1 Shusterman V, Goldberg A, London B. Upsurge in T-wave alternans and nonalternating repolarization instability precedes spontaneous initiation of ventricular tachyarrhythmias in humans. Circulation. 2006; 113: 2880–2887.LinkGoogle Scholar2 Shusterman V, Aysin B, Gottipaty V, Weiss R, Brode S, Schwartzman D, Anderson KP. Autonomic nervous system activity and the spontaneous initiation of ventricular tachycardia. ESVEM Investigators. Electrophysiologic Study Versus Electrocardiographic Monitoring Trial. J Am Coll Cardiol. 1998; 32: 1891–1899.CrossrefMedlineGoogle Scholar3 Shusterman V, Goldberg A. Tracking repolarization dynamics in real-life data. J Electrocardiol. 2004; 37 (suppl): 180–186.CrossrefMedlineGoogle Scholar4 Bloomfield DM, Hohnloser SH, Cohen RJ. Interpretation and classification of microvolt T wave alternans tests. J Cardiovasc Electrophysiol. 2002; 13: 502–512.CrossrefMedlineGoogle Scholar Previous Back to top Next FiguresReferencesRelatedDetails January 2, 2007Vol 115, Issue 1 Advertisement Article InformationMetrics https://doi.org/10.1161/CIRCULATIONAHA.106.650952PMID: 17200449 Originally publishedJanuary 2, 2007 PDF download Advertisement SubjectsArrhythmias

Background: Diabetes mellitus (DM) is a well recognised cardiovascular risk factor, associated with adverse clinical outcomes. Microvolt T wave alternans (TWA) and to some extent alternans of the QRS complex (QRSA) during stress ECG testing have the ability to detect patients at an increased risk for sudden cardiac death (SCD). The results about the influence of DM on TWA during stress testing are controversial, and studies regarding QRSA values during stress ECG testing according to the presence or absence of DM are lacking. The purpose of the present study is to evaluate the influence of DM on TWA and QRSA during stress ECG testing. Methods: Principal component analysis, combined with wave amplitude computation was used for TWA and QRSA quantification. We studied 107 patients ( 63 ± 10 years, 42% male). DM was present in 36% and angiographically significant coronary artery disease (AS-CAD) in 45%. Results: Patients with DM had higher QRSA compared to non-diabetics (P = 0.001); TWA did not differ significantly. Patients with positive stress ECG tests had higher TWA and QRSA compared to those with negative stress tests (P less than 0.001), no matter of the presence or absence of DM. In non-diabetics TWA and QRSA values were significantly higher when percutaneous coronary intervention (PCI) was performed (P less than or equal to 0.001), while in the diabetic subgroup such a difference was attenuated to no significant. Conclusions: Diabetics had higher QRSA during stress ECG testing, while TWA values were not significantly influenced by the diabetic status. The presence of DM attenuates the observed differences of TWA and QRSA between patients with or without PCI. J Endocrinol Metab. 2012;2(1):32-38 doi: https://doi.org/10.4021/jem69e

T-wave alternans (TWA) allows for identification of patients at an increased risk of ventricular arrhythmia. Stress test, which increases heart rate in controlled manner, is used for TWA measurement. However, the TWA detection and analysis are often disturbed by muscular interference. The evaluation of wavelet based denoising methods was performed to find optimal algorithm for TWA analysis. ECG signals recorded in twelve patients with cardiac disease were analyzed. In seven of them significant T-wave alternans magnitude was detected. The application of wavelet based denoising method in the pre-processing stage increases the T-wave alternans magnitude as well as the number of BSPM signals where TWA was detected. Keywords: T-wave alternans, wavelet denoising, stress test, arrhythmia, sudden cardiac death