T-wave Alternans Detection Research Articles

COMPARISON OF T WAVE ALTERNANS IN PATIENTS WITH ISCHEMIC AND NON-ISCHEMIC CARDIOMYOPATHY

Background: Early detection of T-wave alternans in patients with cardiomyopathy can help in risk stratification of ventricular arrhythmias leading to sudden cardiac death. The present study was designed to compare T-wave alternans in patients with ischemic and non-ischemic cardiomyopathy. Methods: This cross-sectional comparative study was carried out at Department of Cardiac Electrophysiology, Armed Forces Institute of Cardiology, Rawalpindi during 2019. Thirty patients with ischemic cardiomyopathy along with equal number of non-ischemic cardiomyopathy of matched age and gender were recruited through non-probability purposive sampling. Patients with diabetes mellitus, cerebrovascular accident, heart failure, bundle branch block, systemic arterial hypertension and ongoing anti-arrhythmic therapy were excluded from the study. DMS 300-4L Holters were used to obtain ambulatory ECG recordings. Cardio Scan Premier 12 Lux software was used for analysis of T-wave alternans. Results: A total of 60 subjects were studied. The mean value of T-wave alternans was 52.73±30.76 µV and 57.47±36.54 µV for patients with ischemic and non-ischemic cardiomyopathy respectively. The difference between mean values was statistically insignificant (p=0.59). T-wave alternans was present in 8 (26.7%) patients with ischemic cardiomyopathy, while 5 (16.7%) patients with non-ischemic cardiomyopathy showed positive T-wave alternans and the difference was statistically insignificant (p=0.35). Conclusion: The mean value of T-wave alternans and frequency of patients with positive T-wave alternans is not significantly different in ischemic and non-ischemic cardiomyopathy. Pak J Physiol 2022;18(1):9?12

Detection of T-wave Alternans in ECG Signals Using FRFT and Tensor Decomposition

T-wave alternans (TWA) refers to the periodic beat-to-beat variation in the amplitude of T-wave in the electrocardiogram (ECG) signal in an ABAB-pattern. TWA has been proven to be a very important indicator of malignant arrhythmia risk stratification. A new method to detect TWA by combining fractional Fourier transform (FRFT) and tensor decomposition is proposed. First, the T-wave vector is extracted from the ECG of each heartbeat, and its FRFT amplitudes at multiple orders are arranged to form a T-wave matrix. Then, a third-order tensor is composed of T-wave matrices of several consecutive heart beats. After tensor decomposition, projection matrices are obtained in three dimensions. The complexity of the projection matrix is measured by Shannon entropy to obtain feature vector to detect the presence of TWA. Results show that the sensitivity, specificity, and accuracy of the algorithm on the MIT-BIH database are 91.16%, 94.25%, and 92.68%, respectively. This method effectively utilizes the fractional domain information of ECG, and shows the promising potential of the FRFT in ECG signal processing.

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

Detection and estimation of t-wave alternans (TWA) in presence of indispensable physiological artifacts is still a challenging task, as in most of the cases, the signal of interest resides well below the noise levels. In this paper, a generalized detection theoretic framework (GDFT) is proposed for the detection and estimation of TWA from the stress test ECG signal. The analytical foundations, TWA signal modeling, and finally simulations of nine TWA detectors and estimators belonging to median match filtering, empirical mode decomposition (EMD) based match filtering, and generalized likelihood ratio test (GLRT) for GDTF are presented. GLRT schemes require noise statistics for parameter estimation and are computationally efficient. GLRT detectors outperform all the detectors including the benchmark spectral method by ≥ 2 dB for a broad spectrum of SNR ranging from -15 dB to 30 dB under Gaussian noise. EMD based strategies also outperform spectral method under Gaussian and Laplacian noise by ≥ 1 dB.

An empirical mode decomposition based detection theoretic strategy for T-wave alternans analysis

T-wave alternans (TWA) is a well-recognized marker for malignant cardiac arrhythmia which may lead to sudden cardiac arrest. Micro-volt TWA detection and estimation becomes a challenge under the presence of process noises, transient outliers and physiological artifacts. This paper proposes an empirical mode decomposition (EMD) based template matched-filter approach (EMF) for TWA analysis, targeting detection of distributed energy in beat-to-beat alternant waveform rather than peak amplitude. The template is dynamically generated through decomposition of alternant waveform into its intrinsic modes and subsequent reconstruction. Consequently, the study proposes six different variants of matched-filter based upon estimated reconstructions of intrinsic mode functions through spectral purity (EMFSPI), detrended fluctuation analysis (EMFDFA), and four different correlation based thresholding schemes (EMFCORR1/2/3/4). Performance evaluation of the proposed strategy and subsequently formulated variants has been done using realistically synthesized noisy data-sets against previously proposed median and mean based template matched-filtering detectors (TMFD). The classical spectral method (SM) has been used as a bench-marking algorithm. All six variants of EMF detectors outperform the previously proposed TMFD variants. The alternant signal and IMF cross-correlation based correlation strategy shows >2 dB improvement as compared to the benchmark. Among correlation based methods, the hard thresholding approach outperforms SM by >1 dB . Subsequent to detection under improved signal conditions, the estimator trends of all the proposed variants are generally comparable with heuristic approaches outperforming the correlation based matched filter estimators.

Dofetilide-Induced Microvolt T-Wave Alternans.

Dofetilide is an antiarrhythmic drug that selectively inhibits the rapid component of the delayed rectifier potassium current. The administration of dofetilide may cause ventricular arrhythmias and torsade de pointes. Electrocardiographic (ECG) microvolt T-wave alternans (TWA), an electrophysiologic phenomenon consisting in the beat-to-beat alternation of the T-wave amplitude requiring computerized algorithms to be detected, has also been associated to malignant ventricular arrhythmias. Aim of the present study was to evaluate if dofetilide induces TWA during the 24 hours following administration. The study population consisted of 22 healthy subjects ("ECG Effects of Ranolazine, Dofetilide, Verapamil, and Quinidine in Healthy Subjects" database by Physionet) to whom a 500 μg-dose of dofetilide was administered. For each subject, 10 s ECG were acquired at baseline (0.5 hour before dofetilide administration) and at 15 time points during the 24 hours following the drug administration. ECG were then processed for automatic TWA detection by correlation method. In 21 subjects out of 22, after dofetilide administration, TWA significantly increased to a peak value (median TWA values went from 6 μV at baseline to a max 32 μV; p<; 0.05), on average after 5 hours, to then come back to values closer to baseline. Thus, in healthy subjects, dofetilide increases occurrence and levels (6 times baseline value on average) of TWA in the hours following its administration.

The roles of mid-myocardial and epicardial cells in T-wave alternans development: a simulation study

BackgroundThe occurrence of T-wave alternans in electrocardiographic signals was recently linked to susceptibility to ventricular arrhythmias and sudden cardiac death. Thus, by detecting and comprehending the origins of T-wave alternans, it might be possible to prevent such events.ResultsHere, we simulated T-wave alternans in a computer-generated human heart model by modulating the action potential duration and amplitude during the first part of the repolarization phase. We hypothesized that changes in the intracardiac alternans patterns of action potential properties would differentially influence T-wave alternans measurements at the body surface. Specifically, changes were simulated globally in the whole left and right ventricles to simulate concordant T-wave alternans, and locally in selected regions to simulate discordant and regional discordant, hereinafter referred to as “regional”, T-wave alternans. Body surface potential maps and 12-lead electrocardiographic signals were then computed. In depth discrimination, the influence of epicardial layers on T-wave alternans development was significantly higher than that of mid-myocardial cells. Meanwhile, spatial discrimination revealed that discordant and regional action potential property changes had a higher influence on T-wave alternans amplitude than concordant changes. Notably, varying T-wave alternans sources yielded distinct body surface potential map patterns for T-wave alternans amplitude, which can be used for location of regions within hearts exhibiting impaired repolarization. The highest ability for T-wave alternans detection was achieved in lead V1. Ultimately, we proposed new parameters Vector Magnitude Alternans and Vector Angle Alternans, with higher ability for T-wave alternans detection when using multi-lead electrocardiographic signals processing than for single leads. Finally, QT alternans was found to be associated with the process of T-wave alternans generation.ConclusionsThe distributions of the body surface T-wave alternans amplitude have been shown to have unique patterns depending on the type of alternans (concordant, discordant or regional) and the location of the disturbance in the heart. The influence of epicardial cells on T-wave alternans development is significantly higher than that of mid-myocardial cells, among which the sub-endocardial layer exerted the highest influence. QT interval alternans is identified as a phenomenon that correlate with T-wave alternans.

A multichannel decision-level fusion method for T wave alternans detection

Sudden cardiac death (SCD) is one of the most prominent causes of death among patients with cardiac diseases. Since ventricular arrhythmia is the main cause of SCD and it can be predicted by T wave alternans (TWA), the detection of TWA in the body-surface electrocardiograph (ECG) plays an important role in the prevention of SCD. But due to the multi-source nature of TWA, the nonlinear propagation through thorax, and the effects of the strong noises, the information from different channels is uncertain and competitive with each other.

Validation of μ-volt T-wave alternans analysis using multiscale analysis-by-synthesis and higher-order SVD

Abstract Detection of microvolt T-wave alternans (TWA) and finding its clinical significance remains a challenging task. In this work, we propose a new TWA detection method based on multiscale analysis-by-synthesis followed by higher-order singular value decomposition (MAS–HOSVD). The multilead ECG (MECG) data, after R-peak detection, were represented as a third-order tensor. Then HOSVD was applied on the subband reconstructed T-wave tensor to determine a tensor containing T-wave information (TensorTinfo). The p-value, TWA ratio, and the probability of detection (PD) were computed, based on the information present in TensorTinfo, to validate the presence of TWA or not. The proposed method was evaluated with semi-synthetic and real signals by adding several types of noise at different SNR levels and was compared to a multilead and two single-lead schemes. Simulation results showed that the proposed method detected TWA accurately with the sensitivity of 95.2% for the alternans level of 10 μV. Also, the method detected TWA with an SNR at least lower by 10 dB than other methods. The MAS–HOSVD method was tested to be accurate in TWA analysis of MECG data. This method can be considered as an automatic tool for prior prognosticator of sudden cardiac death, consequently, can help reduce the mortality rate.

A multichannel decision-level fusion method for T wave alternans detection.

Sudden cardiac death (SCD) is one of the most prominent causes of death among patients with cardiac diseases. Since ventricular arrhythmia is the main cause of SCD and it can be predicted by T wave alternans (TWA), the detection of TWA in the body-surface electrocardiograph (ECG) plays an important role in the prevention of SCD. But due to the multi-source nature of TWA, the nonlinear propagation through thorax, and the effects of the strong noises, the information from different channels is uncertain and competitive with each other. As a result, the single-channel decision is one-sided while the multichannel decision is difficult to reach a consensus on. In this paper, a novel multichannel decision-level fusion method based on the Dezert-Smarandache Theory is proposed to address this issue. Due to the redistribution mechanism for highly competitive information, higher detection accuracy and robustness are achieved. It also shows promise to low-cost instruments and portable applications by reducing demands for the synchronous sampling. Experiments on the real records from the Physikalisch-Technische Bundesanstalt diagnostic ECG database indicate that the performance of the proposed method improves by 12%-20% compared with the one-dimensional decision method based on the periodic component analysis.

Automatic detection of T wave alternans using tensor decompositions in multilead ECG signals

Objective: T wave alternans (TWA) is a promising non-invasive risk stratification tool for sudden cardiac death which can be detected from surface ECG. This paper proposes a novel method to automatically detect TWA based on tensor decomposition methods. Approach: Two different tensor decomposition approaches are examined and compared, namely canonical polyadic decomposition and the more generalized variation PARAFAC2 which allows the T waves to shift in time. Results and Significance: Results on different artificial and clinical signals show that the presented methods are a robust and reliable way for TWA detection, and show the potential benefit of tensors in ECG signal processing.

A Potential-Based Inverse Spectral Method to Noninvasively Localize Discordant Distributions of Alternans on the Heart From the ECG.

Image the spatial distribution of TWA on the heart surface from ECG measurements. We introduced the inverse spectral method (ISM), a tailored inverse (or ElectroCardioGraphic Imaging) solution designed specifically to noninvasively image cases of TWA on the heart. We evaluate the ISM on its capacity to reliably detect the spatial distributions of TWA compared against a standard TWA detection method applied directly to the electrograms on the heart surface. We report on results from both a series of synthetic simulations of TWA generated using the ECGSIM software and a set of continuous epicardial surface voltage recordings from a canine experiment. ISM detected TWA distributions that matched the phase of the true underlying out-of-phase regions over and of the heart surface, respectively. Our results suggest that ISM is capable of reliably detecting the different regions present in a TWA distribution across a wide variety of TWA locations on the heart in simulation and in the face of transients and nonidealities in the canine recordings.

Multi-lead Fusion Detection of T-Wave Alternans Using Dezert-Smarandache Theory

T-Wave alternans (TWA) is a cardiac phenomenon regarded as an index of high risk of sudden cardiac death (SCD). Although a number of methods have been proposed for TWA detection, their final decision always fully depends on one single lead which is usually picked out for its strongest TWA detection result among all the other leads. That is to say that lots of useful information have been unused and wasted. To our best knowledge, no method that fuses TWA detection results independently obtained by each lead to do comprehensive decision has been introduced. In this paper, a novel multi-lead method for TWA fusion detection is proposed.

Benchmarking of a T-wave alternans detection method based on empirical mode decomposition

Background and objective: T-wave alternans (TWA) is a fluctuation of the ST-T complex occurring on an every-other-beat basis of the surface electrocardiogram (ECG). It has been shown to be an informative risk stratifier for sudden cardiac death, though the lack of gold standard to benchmark detection methods has promoted the use of synthetic signals. This work proposes a novel signal model to study the performance of a TWA detection. Additionally, the methodological validation of a denoising technique based on empirical mode decomposition (EMD), which is used here along with the spectral method, is also tackled.Methods: The proposed test bed system is based on the following guidelines: (1) use of open source databases to enable experimental replication; (2) use of real ECG signals and physiological noise; (3) inclusion of randomized TWA episodes. Both sensitivity (Se) and specificity (Sp) are separately analyzed. Also a nonparametric hypothesis test, based on Bootstrap resampling, is used to determine whether the presence of the EMD block actually improves the performance.Results: The results show an outstanding specificity when the EMD block is used, even in very noisy conditions (0.96 compared to 0.72 for SNR = 8 dB), being always superior than that of the conventional SM alone. Regarding the sensitivity, using the EMD method also outperforms in noisy conditions (0.57 compared to 0.46 for SNR=8 dB), while it decreases in noiseless conditions.Conclusions: The proposed test setting designed to analyze the performance guarantees that the actual physiological variability of the cardiac system is reproduced. The use of the EMD-based block in noisy environment enables the identification of most patients with fatal arrhythmias.

The clinical value of T-wave alternans derived from Holter monitoring.

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.

Beat-to-beat T-wave alternans detection using the Ensemble Empirical Mode Decomposition method

Background: T-wave alternans (TWA) is defined as a consistent variation in the repolarization morphology that repeats on every other beat. This study aimed to evaluate beat-to-beat TWA detection using the Ensemble EMD (EEMD) method. Method: A total of 108 recordings of standard 12-lead ECGs of 69 healthy subjects (17 females, 42±18 years; 52 males, 40±13 years) and 39 cardiac-condition patients (ischemic cardiomyopathy; ICM and dilated cardiomyopathy; DCM) with left ventricular ejection fractions (LVEF) ≤40% were studied. We first determined the QT interval of ECG via a template matching algorithm. Then, beat-to-beat T-waves were extracted to quantify beat-to-beat TWA. The EEMD method was applied to the T-wave time series to decompose them into a set of intrinsic mode functions (IMFs). The instantaneous frequency was measured by performing the Hilbert transform on the selected IMF for extracting the features. Four different classifiers were applied to the extracted features to assess and classify the existence of TWA in the ECG signal. Results: In the simulation study, the global classifier worked better than the subject-based classifier for detecting alternans in the T-waves. In addition, the average accuracy and sensitivity for detecting TWA were greater than 80%. In the real Holter ECG data obtained from Toronto General Hospital, the Ensemble classifier had higher classification accuracy, 74%, than other classifiers and a positive predictive value of 100%. Conclusion: In conclusion, the proposed Ensemble EMD method with Ensemble classifier can be utilized for detecting beat-to-beat TWA in the ECG signal.

Microvolt T-wave Alternans: Where Are We Now?

Microvolt T-wave alternans (TWA), characterised as beat-to-beat fluctuation of T-wave amplitude and morphology, is an electrophysiological phenomenon associated clinically with impending ventricular arrhythmias and is an important marker of arrhythmia risk. Currently, two main methods for the detection of TWA exist, namely, the spectral method and the time-domain modified moving average method; both are discussed in this review. Microvolt TWA has been associated with cardiovascular mortality and sudden cardiac death in several clinical studies involving >14,000 subjects with reduced as well as preserved left ventricular function. Although TWA appears to be a useful marker of susceptibility for lethal ventricular arrhythmias and cardiovascular death, so far there is no sufficient evidence from randomised clinical trials to support its use in guiding therapy. However, several ongoing trials are expected to provide more information about the clinical use of TWA testing.

Improved robust T-wave alternans detectors.

New statistical and spectral detectors, the modified matched pairs t test, the extended spectral method and the modified spectral method, were proposed for T-wave alternans (TWA) detection gaining robustness according to trend and single-frequency interferences. They were compared to classic detectors such as matched pairs t test, unpaired t test, spectral method, generalized likelihood ratio test and estimated TWA amplitude within a simulation framework and applied to real data. The optimal detection threshold was selected by using a full Monte-Carlo simulation where signals, with and without alternans episodes, were corrupted by Gaussian noise with different power and single-frequency interferences with different tones. All the combinations of noise and frequency were selected and repeated 500 times in order to compute probability of detection ([Formula: see text]) and the false alarm probability ([Formula: see text]), providing ROC curves. The study group consisted of 50 patients with implantable cardioverter-defibrillator (age: [Formula: see text]; LVEF: [Formula: see text]), who were paced (ventricular pacing) at 100 bpm. Two-minute recordings were analyzed. The XYZ orthogonal lead system was used. The best performance was reached by using the modified matched pairs t test (in comparison with the spectral method and other reference methods).

Detection of T-Wave Alternans in ECGs by Wavelet Analysis

T wave alternans (TWA) is an acute and serious problem associated with the heart and mostly they are reflected in Electrocardiogram (ECG). It is identified as an alteration of the amplitude and morphology of the T wave that occurs in every other beat indicating electrical instability in acute ischemia, where it may precede ventricular tachyarrhythmia. The analysis of TWA is introduced recently as a new diagnostic tool for identification of patients with an increased risk of ventricular tachyarrhythmia or sudden cardiac death. ECGs recorded by sophisticated equipment with specific software can only diagnose TWA. The hard and software are exorbitantly costlier and therefore not available everywhere. In the present study, the TWA is brought out very well from the amplitude of continuous Wavelet transform (CWT) which is not only cheap and simple but also highly reliable and may be considered as an alternate tool for such investigations. This study is also substantiated by the exclusively proposed new mother wavelet namely ECG wavelet. The results are presented highlighting the salient features.

Effect of a multi-lead PCA approach on modified moving average method for T-wave alternans detection

T-wave alternans, a beat-to-beat re-polarization pattern variation phenomenon, plays an important role in sudden cardiac death prediction. The proposed method modifies the currently available Modified Moving Average Method (MMAM) by a multi-lead principal component analysis (PCA) approach. Application of PCA will concentrate the alternans effect, which is supposed to be distributed on multiple leads in a single derived lead. MMAM is applied to the derived lead and alternans magnitude is measured. Results are compared with MMAM, implemented by the authors and by an open source tool, TWAnalyser. The TWA Physionet/CinC 2008 Challenge Database is used for testing. Performances are measured in terms of root mean square error (rmse) and correlation coefficients. The rmse is highest for TWAnalyser results and lowest for the proposed method. While the correlation coefficient is highest for the proposed method, 0.88, that for normal MMAM is 0.73 and for TWAnalyser is 0.75. The proposed multi-lead PCA approach provides improved performance of current MMAM.

A template matched-filter based scheme for detection and estimation of t-wave alternans

Microvolt T-wave alternans (TWA) is among the important risk stratifiers for sudden cardiac death, having significant prognostic ability. A new detection theoretic technique, called template matched-filter detector (TMFD), is proposed for TWA detection and estimation. Unlike classical schemes, which estimate the TWA amplitude directly, the proposed strategy targets alternant energy within the repolarization complexes. The scheme is evaluated using realistically synthesized ECG signals and alternant waveforms with varying temporal distributions, synthetic noises and real artifacts. The implementation of TMFD is discussed in the context of two different templates, i.e., median (TMFD-1) and mean (TMFD-2). Detection performance and estimation accuracy is validated against three classical schemes, i.e., spectral method (SM), modified moving average method (MMAM) and correlation method (CM). The proposed scheme outperforms CM over the entire range of SNR with both the templates and all noise types. Detection performance of TMFD approaches SM when the signal is corrupted with real artifacts. In Gaussian noise, performance of TMFD-1 is comparable with SM when detection probability is maximized, and is 2dB degraded for Laplacian noise. In terms of estimation accuracy, SM has the least value of bias for all noise types over the low range of SNR. In better signal conditions (SNR = 25dB), the bias of TMFD approaches SM for alternant magnitude > 40μV in the Gaussian case and for magnitudes > 20μV with real noises. The bias of TMFD for real noises is comparable to MMAM for all TWA amplitudes and there is a gap of approximately 20μV under Gaussian noise for SNR = 10dB. The proposed scheme is also promising for dynamic TWA tracking as it successfully indicates temporal origins and termini of transient alternan episodes through beat to beat estimation statistics.