Why the study?
Does a linear least squares method algorithm improve the accuracy and robustness of T-wave alternans detection compared to the modified moving average method?
Does a linear least squares method algorithm improve the accuracy and robustness of T-wave alternans detection compared to the modified moving average method?
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A novel linear least squares method for T-wave alternans analysis demonstrates high accuracy and robustness, correlating well with established methods.
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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Wan et al. (2012) studied this question.
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