Key result
Optimization of signal processing stages reduced the power of T-Wave Alternans amplitude estimation errors by 34.0% for the temporal method and 5.2% for the spectral method in semisynthetic signals.
Systematic optimization of signal processing blocks using nonparametric hypothesis testing significantly improves the accuracy of T-Wave Alternans detection and estimation algorithms.
May improve TWA estimation precision in research; leaves open clinical validation and outcome relevance.
Although a number of methods have been proposed for T-Wave Alternans (TWA) detection and estimation, their performance strongly depends on their signal processing stages and on their free parameters tuning. The dependence of the system quality with respect to the main signal processing stages in TWA algorithms has not yet been studied. This study seeks to optimize the final performance of the system by successive comparisons of pairs of TWA analysis systems, with one single processing difference between them. For this purpose, a set of decision statistics are proposed to evaluate the performance, and a nonparametric hypothesis test (from Bootstrap resampling) is used to make systematic decisions. Both the temporal method (TM) and the spectral method (SM) are analyzed in this study. The experiments were carried out in two datasets: first, in semisynthetic signals with artificial alternant waves and added noise; second, in two public Holter databases with different documented risk of sudden cardiac death. For semisynthetic signals (SNR = 15 dB), after the optimization procedure, a reduction of 34.0% (TM) and 5.2% (SM) of the power of TWA amplitude estimation errors was achieved, and the power of error probability was reduced by 74.7% (SM). For Holter databases, appropriate tuning of several processing blocks, led to a larger intergroup separation between the two populations for TWA amplitude estimation. Our proposal can be used as a systematic procedure for signal processing block optimization in TWA algorithmic implementations.
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Goya–Esteban et al. (2014) studied T-Wave Alternans. Optimization of signal processing stages using nonparametric hypothesis testing vs. Unoptimized TWA analysis systems was evaluated on Power of TWA amplitude estimation errors and error probability. Optimization of signal processing stages reduced the power of T-Wave Alternans amplitude estimation errors by 34.0% for the temporal method and 5.2% for the spectral method in semisynthetic signals.
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