Comparative analysis of methods for automatic detection and quantification of microvolt T-wave alternans

Abstract

Microvolt T-wave alternans (TWA), consisting of every-other-beat changes in ECG T-wave morphology, is an index of susceptibility to malignant ventricular arrhythmias, requiring automatic techniques to be identified. Five of these, namely, fast-Fourier-transform spectral method (FFTSM), complex-demodulation method (CDM), modified-moving-average method (MMAM), Laplacian-likelihood-ratio method (LLRM) and adaptive-match-filter method (AMFM), were applied here to simulated and sample clinical data. The aim was to compare individual methods ability to properly identify stationary and time-varying TWA, avoiding false-positive detections. The MMAM provided false-positive TWA when applied to simulated ECGs affected by amplitude variability, but TWA. Stationary TWA was properly quantified by the MMAM and, occasionally, underestimated by all other methods. The AMFM properly identified time-varying TWA. By contrast, the FFTSM detected not-stationary TWA as stationary, the MMAM introduced a time-delay in the estimated TWA-amplitude signal, while the CDM and LLRM were reliable only in the presence of slow-varying TWA. Altogether, the AMFM accomplished the best compromise between the needs to avoid false-positive TWA and to detect and characterize true-positive TWA. Results of our simulation approach were useful to explain different TWA levels measured by each competing methods applied to sample Holter ECGs from healthy subjects and coronary artery disease patients.