A time-domain hybrid analysis method for detecting and quantifying T-wave alternans.

T-wave alternans (TWA) in surface electrocardiograph (ECG) signals has been recognized as a marker of cardiac electrical instability and is hypothesized to be associated with increased risk for ventricular arrhythmias among patients. A novel time-domain TWA hybrid analysis method (HAM) utilizing the correlation method and least squares regression technique is described in this paper. Simulated ECGs containing artificial TWA (cases of absence of TWA and presence of stationary or time-varying or phase-reversal TWA) under different baseline wanderings are used to test the method, and the results show that HAM has a better ability of quantifying TWA amplitude compared with the correlation method (CM) and adapting match filter method (AMFM).

[The detection of non-stationary T-wave alternans: a modified correlation method].

T-wave alternans (TWA) refers to a phenomenon appearing in the surface electrocardiograph (ECG) as a consistent fluctuation in morphology and amplitude of the T wave on an "every-other-beat" basis. Correlation method (CM) has a certain ability to detect the non-stationary TWA, but it is very sensitive to noise. In this paper we propose a modified correlation method to ensure a stable and accurate detection of non-stationary TWA.

A combined algorithm for T-wave alternans qualitative detection and quantitative measurement.

Background: T-wave alternans (TWA) provides a noninvasive and clinically useful marker for the risk of sudden cardiac death (SCD). Current most widely used TWA detection algorithms work in two different domains: time and frequency. The disadvantage of the spectral analytical techniques is that they treat the alternans signal as a stationary wave with a constant amplitude and a phase.