BACKGROUND AND PURPOSE: Doppler emboli detection is an established technique, but the nature of the underlying embolic material remains unclear. The intensity and spectral distribution of emboli signals could help to distinguish between signals arising from formed and gaseous emboli. We undertook this study to develop and evaluate a differentiation algorithm based on the spectral characteristics of emboli signals. Subsequently the algorithm was applied to patients with mechanical prosthetic cardiac valves. METHODS: Emboli signals detected in patients with carotid disease, acute stroke, and atrial fibrillation were used as formed emboli data, and signals detected in patients undergoing cardiac catheterization studies were used as gaseous emboli data. For each embolus signal, the maximal amplitude, the sum of amplitudes, and the spectral intensity distribution were calculated. Two hundred emboli signals from each category were used to develop a differentiation algorithm, which was subsequently evaluated on 501 additional solid and 995 gaseous emboli signals. The same algorithm was used to analyze 5958 emboli signals detected in 60 patients with mechanical prosthetic valves. RESULTS: The best results were obtained with an algorithm based on both the maximal amplitude and the sum of amplitudes (sensitivity, 99%; specificity, 96.5%). On subsequent evaluation, the sensitivity and specificity of the algorithm were 99.6% and 89.8%, respectively. Of the 5958 emboli signals detected in prosthetic valve patients, 92.4% were classified as gaseous. CONCLUSIONS: Differentiation between gaseous and formed emboli signals, as detected by transcranial Doppler in vivo, is feasible by means of spectral analysis. Application of the differentiation algorithm in prosthetic valve patients suggests that the embolic material in these patients is gaseous. The possibility of distinguishing between different formed embolic materials with this technique remains to be evaluated.
BACKGROUND AND PURPOSE: Doppler emboli detection is an established technique, but the nature of the underlying embolic material remains unclear. The intensity and spectral distribution of emboli signals could help to distinguish between signals arising from formed and gaseous emboli. We undertook this study to develop and evaluate a differentiation algorithm based on the spectral characteristics of emboli signals. Subsequently the algorithm was applied to patients with mechanical prosthetic cardiac valves. METHODS: Emboli signals detected in patients with carotid disease, acute stroke, and atrial fibrillation were used as formed emboli data, and signals detected in patients undergoing cardiac catheterization studies were used as gaseous emboli data. For each embolus signal, the maximal amplitude, the sum of amplitudes, and the spectral intensity distribution were calculated. Two hundred emboli signals from each category were used to develop a differentiation algorithm, which was subsequently evaluated on 501 additional solid and 995 gaseous emboli signals. The same algorithm was used to analyze 5958 emboli signals detected in 60 patients with mechanical prosthetic valves. RESULTS: The best results were obtained with an algorithm based on both the maximal amplitude and the sum of amplitudes (sensitivity, 99%; specificity, 96.5%). On subsequent evaluation, the sensitivity and specificity of the algorithm were 99.6% and 89.8%, respectively. Of the 5958 emboli signals detected in prosthetic valve patients, 92.4% were classified as gaseous. CONCLUSIONS: Differentiation between gaseous and formed emboli signals, as detected by transcranial Doppler in vivo, is feasible by means of spectral analysis. Application of the differentiation algorithm in prosthetic valve patients suggests that the embolic material in these patients is gaseous. The possibility of distinguishing between different formed embolic materials with this technique remains to be evaluated.
Authors: Caroline Banahan; Zach Rogerson; Clément Rousseau; Kumar V Ramnarine; David H Evans; Emma M L Chung Journal: Ultrasound Med Biol Date: 2014-09-11 Impact factor: 2.998