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

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.

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Correlation method versus enhanced modified moving average method for automatic detection of T-wave alternans

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2010.01.008 ◽

2010 ◽

Vol 98 (1) ◽

pp. 94-102 ◽

Cited By ~ 15

Author(s):

Laura Burattini ◽

Silvia Bini ◽

Roberto Burattini

Keyword(s):

Average Method ◽

Moving Average ◽

Correlation Method ◽

Automatic Detection ◽

T Wave ◽

T Wave Alternans

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Estimation of T-wave alternans from multi-lead ECG signals using a modified moving average method

2008 Computers in Cardiology ◽

10.1109/cic.2008.4749092 ◽

2008 ◽

Author(s):

G.M. Nijm ◽

S. Swiryn ◽

A.C. Larson ◽

A.V. Sahakian

Keyword(s):

Average Method ◽

Moving Average ◽

Ecg Signals ◽

T Wave ◽

T Wave Alternans

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Modified Moving Average T-wave alternans cutpoints

Indian Pacing and Electrophysiology Journal ◽

10.1016/j.ipej.2021.01.009 ◽

2021 ◽

Author(s):

Richard L. Verrier

Keyword(s):

Moving Average ◽

T Wave ◽

T Wave Alternans

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Modified moving average analysis of T-wave alternans to predict ventricular fibrillation with high accuracy

Journal of Applied Physiology ◽

10.1152/japplphysiol.00592.2001 ◽

2002 ◽

Vol 92 (2) ◽

pp. 541-549 ◽

Cited By ~ 242

Author(s):

Bruce D. Nearing ◽

Richard L. Verrier

Keyword(s):

Ventricular Fibrillation ◽

Moving Average ◽

Motion Artifact ◽

Accurate Estimate ◽

T Wave ◽

Average Analysis ◽

Experimental Laboratory ◽

Nonstationary Data ◽

T Wave Alternans ◽

And Performance

T-wave alternans is a marker of cardiac electrical instability with the potential for arrhythmia risk stratification. The modified moving average method was developed to measure alternans in settings with artifacts, noise, and nonstationary data. Algorithms were developed and performance characteristics were validated with simulated electrocardiograms (ECGs). Experimental laboratory ECGs with dynamically changing alternans values were analyzed. Alternans values estimated by modified moving average analysis correlated strongly with input alternans values ( r 2 = 0.9999). Rapidly changing alternans levels and phase reversals did not perturb the measurement. When heart rate was increased from 60 to 180 beats/min, with T-wave alternans apex moving from 237 to 103 ms after the R wave, the measured alternans peak varied <5% from input value. Simulated 50- to 1,000-μV motion artifact spikes typical of treadmill ECGs produced inaccuracies <2%. Alternans values in experimental laboratory study using standard electrodes tracked vulnerability to myocardial ischemia-induced ventricular fibrillation with 100% sensitivity and specificity at a cut point of 0.75 mV. Modified moving average analysis is a robust method that precisely measures T-wave alternans in settings with artifacts, noise, and nonstationary data typical of clinical ECGs and yields an accurate estimate of risk for ventricular fibrillation.

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Detecting and Quantifying T Wave Alternans Using Linear Least Squares Curve Fitting Method

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.239-240.1404 ◽

2012 ◽

Vol 239-240 ◽

pp. 1404-1408

Author(s):

Xiang Kui Wan ◽

Kang Hui Yan ◽

Ming Gui Li

Keyword(s):

Least Squares ◽

Least Squares Method ◽

Moving Average ◽

Correlation Coefficients ◽

Linear Least Squares ◽

T Wave ◽

Patients At Risk ◽

Invasive Method ◽

T Wave Alternans ◽

Curve Fitting Method

Evaluatement of T-wave alternans (TWA) is a non-invasive method to identify patients at risk for sudden cardiac death. A novel time-domain algorithm based on linear least squares method (LSM) for TWA analysis is developed in this paper. And the LSM’s ability to TWA analaysis in comparison with the modified moving average (MMA) method was demonstrated. In a simulation study LSM and MMA can both detected TWA with high accuracy, but under lower SNR levels LSM was found more robust, and its evaluated TWA values are closer to true TWA value. The algorithm was subsequently used to analyze the clinical ECGs from T-Wave Alternans Challenge Database, which showed that the results from this method vs. MMA had the correlation coefficients of 0.89.

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Improved modified moving average analysis of T-wave alternans using least squares-based curve fitting method

International Journal of Biomathematics ◽

10.1142/s1793524514500442 ◽

2014 ◽

Vol 07 (04) ◽

pp. 1450044 ◽

Cited By ~ 2

Author(s):

Xiang-Kui Wan ◽

Kang-Hui Yan ◽

Ang Li ◽

Ming-Hu Wu

Keyword(s):

Curve Fitting ◽

Moving Average ◽

Least Square Method ◽

Least Square ◽

T Wave ◽

Fitting Method ◽

Average Analysis ◽

T Wave Alternans ◽

Improved Performance ◽

Curve Fitting Method

T-wave alternans (TWA) in surface electrocardiograph (ECG) signal is considered a marker of abnormal ventricular function which may be associated with ventricular tachycardia. Several methods have been developed in recent years to evaluate the important feature. One such method is known as modified moving average (MMA) analysis, which performs well for different levels of TWA, but it is sensitive to the noise in T-waves. In this paper we propose an improved MMA algorithm, which adds a stage of T-wave curve fitting for the MMA method before intermediate averaging. The curve fitting is performed by means of least square method technique. Our assessment study demonstrates the improved performance.

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