Time-scale detection of microemboli in flowing blood with Doppler ultrasound.

Small formed elements and gas bubbles in flowing blood, called microemboli, can be detected using Doppler ultrasound. In this application, a pulsed constant-frequency ultrasound signal insonates a volume of blood in the middle cerebral artery, and microemboli moving through its sample volume produce a Doppler-shifted transient reflection. Current detection methods include searching for these transients in a short-time Fourier transform (STFT) of the reflected signal. However, since the embolus transit time through the Doppler sample volume is inversely proportional to the embolus velocity (Doppler-shift frequency), a matched-filter detector should in principle use a wavelet transform, rather than a short-time Fourier transform, for optimal results. Closer examination of the Doppler shift signals usually shows a chirping behavior apparently due to acceleration or deceleration of the emboli during their transit through the Doppler sample volume. These variations imply that a linear wavelet detector is not optimal. We apply linear and quadratic time-frequency and time-scale detectors to a set of noise-corrupted embolus data. Our results show improvements of about 1 dB using the time-scale detectors versus an STFT-based detector signifying that embolus detection is best approached as a time-scale problem. A time-scale-chirp detector is also applied and is found to have the overall best performance by about 0.5-0.7 dB while coming fairly close (about 0.75 dB) to a theoretical upper bound.

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 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.