This paper addresses the detection of emboli in transcranial Doppler ultrasound data acquired from an original portable device. The challenge is the removal of several artifacts (motion and voice) intrinsically related to long-duration (up to 1 h 40 mn per patient) outpatient signals monitoring from this device, as well as high intensities due to the stochastic nature of blood flow. This paper proposes an adapted removal procedure. This firstly consists of reducing the background noise and detecting the blood flow in the time–frequency domain using a likelihood method for contour detection. Then, a hierarchical extraction of features from magnitude and bounding boxes is achieved for the discrimination of emboli and artifacts. After processing of the long-duration outpatient signals, the number of artifacts predicted as emboli is considerably reduced (by 92% for some parameter values) between the first and the last step of our algorithm.