International audienceDetection of small emboli, precursors of Cerebrovascular Accidents, is a worldwide concern since CVAs represent the second cause of mortality in the world. Computerized analysis of Transcranial Doppler signals can aid early detection of circulating emboli and micro-emboli. Commercially used systems of automatic emboli detection rely on standard short time Fourier transform techniques in which detection is based on constant thresholds. These standard algorithms do not offer robust detections and are incapable of detecting the smallest micro-emboli. To enhance this detection, we propose in this study optimized techniques based on novel methods of threshold application. By implementing our new time-varying threshold of detection, we were able to decrease the probability of non-detection and the probability of false alarm by around half the values obtained by standard techniques. Moreover, our new techniques were clearly efficient in exploiting the transient-like embolic signals and hence make detection of micro-emboli easier and more evident. This was proved by enhancing important parameters of which are the embolus to blood ratio and the peak to threshold ratio. Applied on our set of recorded signals, the new detectors allowed obtaining embolus to blood ratios twice greater than the embolus to blood ratios achieved by standard techniques and a sufficient increase in peak to threshold ratios

Robust detection of the smallest circulating cerebral microemboli is an efficient way of preventing strokes, which is second cause of mortality worldwide. Transcranial Doppler ultrasound is widely considered the most convenient system for the detection of microemboli. The most common standard detection is achieved through the Doppler energy signal and depends on an empirically set constant threshold. On the other hand, in the past few years, higher order statistics have been an extensive field of research as they represent descriptive statistics that can be used to detect signal outliers. In this study, we propose new types of microembolic detectors based on the windowed calculation of the third moment skewness and fourth moment kurtosis of the energy signal. During energy embolus-free periods the distribution of the energy is not altered and the skewness and kurtosis signals do not exhibit any peak values. In the presence of emboli, the energy distribution is distorted and the skewness and kurtosis signals exhibit peaks, corresponding to the latter emboli. Applied on real signals, the detection of microemboli through the skewness and kurtosis signals outperformed the detection through standard methods. The sensitivities and specificities reached 78% and 91% and 80% and 90% for the skewness and kurtosis detectors, respectively.