Task: Intent Identification
Input (English): The detection of asymptomatic embolic signals by Doppler ultrasound may offer a powerful investigational tool in the management of cerebrovascular disease. However, early studies, particularly in patients with carotid artery disease, have reported very different frequencies of embolic signals. While this may reflect differences in patient groups and the criteria used for embolic signal identification, the degree of time-window overlap may be important. If this is insufficient, some embolic signals may fall between two time windows and not appear on the spectral display. Furthermore, the use of nonrectangular time windows, such as the Hanning window, may result in variation of the intensity of an embolic signal depending on where it is detected within the time window.', 'To test the importance of this potential problem, the same 25 embolic signals recorded as the audio signal on digital audiotape were each played repeatedly through a transcranial Doppler ultrasound (TCD) system using fast Fourier transform analysis. An older system with no time-window overlap was used, and a more modern system was also used in which three different degrees of overlap were used: -9%, 27%, and 57%. The number of signals audible but not appearing on the spectral display was recorded. The variability in the relative intensity increase for the same embolic signal played repeatedly was estimated by calculating the coefficient of variation of the relative intensity increase.', 'With the older system, 39/500 (7.8%) of embolic signals were missed. With the newer system, the number of embolic signals missed was fewer and decreased with increasing degrees of overlap (10/500 for -9% overlap, 1/500 for 27% overlap, and 0/500 for 57% overlap). For those setups in which embolic signals were missed, there was a highly significant relationship between duration of embolic signal and number of signals missed. In parallel with these results, the coefficient of variation of the relative intensity increase became progressively less with increasing degrees of time-window overlap. For all processing setups, the coefficient of variation was greater for the less intense and shorter duration signals, but this dependence, as estimated by the slope of the regression line, became less strong with higher degrees of overlap.
Output (English): 1