Improved time-frequency filtering of signal-averaged ECGs

A recently proposed time-frequency filtering technique has shown promising results for the enhancement of signal-averaged electrocardiograms. This method weights the short-time Fourier transform of the ensemble-averaged signal, analogous to the spectral domain Wiener filtering of stationary signals. In effect, it is a self-designing, time-varying Wiener filter applied to the high-resolution electrocardiogram (HRECG). In this study, the authors empirically show that the performance of the proposed technique is about 2–3 dB lower over the critical late-potential portion of the HRECG than the optimal fixed-window, time-frequency filter based on ideal a priori knowledge of statistics. Although this ideal knowledge and performance is unattainable in practice, these results suggest that there remains potential for modest improvement. To narrow this gap in performance, improvements based on alternative structures for the time-frequency filter, including time-varying short-time Fourier transform windows, are proposed. Simulation results show that an improved fixed-window technique can potentially yield an improvement of about 1–1.5 dB. By using properly chosen time-varying windows, the performance could potentially be improved even further. Thus, the improved techniques could produce an HRECG using fewer averages than the existing method, or that could tolerate a lower initial signal-to-noise ratio.